Scientific article

Constraining the Electron Densities in DFT Method as an Effective Way for Ab Initio Studies of Metal-Catalyzed reactions

Published inJournal of computational chemistry, vol. 21, no. 16, p. 1554-1561
Publication date2000

The use of hybrid ab initio QM/MM methods in studies of metalloenzymes and related systems presents a major challenge to computational chemists. Methods that include the metal ion in the quantum mechanical region should also include the ligands of the metal in this region. Such a treatment, however, should be very demanding if one is interested in performing the configurational averaging needed for proper calculations of activation free energies. In the present work we examine the ability of the frozen DFT (FDFT) and the constrained DFT (CDFT) approaches to be used in ab initio studies of metal-catalyzed reactions, while allowing for an effective QM (rather than a QM/MM) treatment of the reacting complex. These approaches allow one to treat the entire enzyme by ab initio DFT methods, while confining the SCF calculations to a relatively small subsystem and keeping the electron density of the rest of the system frozen (or constrained). It is found that the FDFT and CDFT models can reproduce the trend obtained by a full DFT calculation of a proton transfer between two water molecules in a (Im)3Zn2+(H2O)2 system. This and related test cases indicate that our approximated models should be capable of providing a reliable representation of the energetics of metalloenzymes. The reasons for the special efficiency of the FDFT approach are clarified, and the strategies that can be used in FDFT studies of metalloenzymes are outlined.

  • Frozen density functional theory (FDFT)
  • Metalloenzyme catalyzed reaction
  • Hybrid quantum mechanical/molecular mechanics (QM/MM) approaches
  • Ab initio free energies
Research group
Citation (ISO format)
HONG, Gongyi et al. Constraining the Electron Densities in DFT Method as an Effective Way for Ab Initio Studies of Metal-Catalyzed reactions. In: Journal of computational chemistry, 2000, vol. 21, n° 16, p. 1554–1561. doi: 10.1002/1096-987X(200012)21:16<1554::AID-JCC12>3.0.CO;2-I
ISSN of the journal0192-8651

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