A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+

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1 Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
2 Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense, Denmark
3 Institute of Theoretical Chemistry, Universität Ulm, Ulm, Germany
DOI
10.7287/peerj.preprints.353v1
Published
Accepted
Subject Areas
Biochemistry, Computational Biology, Computational Science
Keywords
computational chemistry, proteins, biochemistry, computational biochemistry, molecular modeling
Copyright
© 2014 Kromann et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Kromann JC, Christensen A, Steinmann C, Korth M, Jensen JH. 2014. A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+. PeerJ PrePrints 2:e353v1

Abstract

We present new dispersion and hydrogen bond corrections to the PM6 method, PM6-D3H+, and its implementation in the GAMESS program. The method combines the DFT-D3 dispersion correction by Grimme et al with a modified version of the H+ hydrogen bond correction by Korth. Overall, the interaction energy of PM6-D3H+ is very similar to PM6-DH2 and PM6-DH+, with RMSD and MAD values within 0.02 kcal/mol of one another. The main difference is that the geometry optimizations of 88 complexes result in 82, 6, 0, and 0 geometries with 0, 1, 2, and $\ge$ 3 imaginary frequencies using PM6-D3H+ implemented in GAMESS, while the corresponding numbers for PM6-DH+ implemented in MOPAC are 54, 17, 15, and 2. The PM6-D3H+ method as implemented in GAMESS offers an attractive alternative to PM6-DH+ in MOPAC in cases where the LBFGS optimizer must be used and a vibrational analysis is needed, e.g. when computing vibrational free energies. While the GAMESS implementation is up to 10 times slower for geometry optimizations of proteins in bulk solvent, compared to MOPAC, it is sufficiently fast to make geometry optimizations of small proteins practically feasible.

Author Comment

This is an initial version.