Implementation and benchmark tests of the DFTB method and its application in the ONIOM method

J. Chem. Theory Comput.

Sasakura

Zheng

et al. 2010

Self Cite

The replacement of standard molecular mechanics force fields by inexpensive molecular orbital (QM') methods in multi-scale models has many advantages, e.g., a more straightforward description of mutual polarization and charge transfer between layers. The ONIOM(QM:QM') scheme with mechanical embedding can combine any two methods without prior parameterization or significant coding effort. In this scheme the environmental effect is evaluated fully at the QM' level and the accuracy therefore depends on how well the low-level QM' method describes the changes in electron density of the reacting region. To examine the applicability of the QM:QM' approach we perform case studies with density- Polarization effects are fairly well described using DFTB, but in some cases QM and QM' methods converge to different electronic states. We discuss when the QM:QM' approach is appropriate and the possibilities of estimating the quality of the ONIOM extension without having to make explicit benchmarks of the entire system. 3

Section: The Acylation Process In Trypsin --Two and Three-layer Camentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Case Studies of ONIOM(DFT:DFTB) and ONIOM(DFT:DFTB:MM) for Enzymes and Enzyme Mimics

J. Chem. Theory Comput.

Sasakura

Zheng

et al. 2010

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“…Another option is to combine two molecular orbital methods that both include charge transfer. [48] Tests of density-functional tight-binding method combined with B3LYP in an ONIOM model gave good results for IPNS, but the requirement is that both molecular orbital methods give the same description of the electronic structure of the iron center. [49,50] QM/MM models typically include polarization of the QM part by the MM charges, called electronic embedding (EE).…”

Section: Low-level Treatment and Interactions Between Layersmentioning

confidence: 99%

Protein effects in non-heme iron enzyme catalysis: insights from multiscale models

Vedin

2016

J Biol Inorg Chem

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PostprintThis is the accepted version of a paper published in Journal of Biological Inorganic Chemistry. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. Citation for the original published paper (version of record):Vedin, N P., Lundberg, M. (2016) Protein effects in non-heme iron enzyme catalysis: insights from multiscale models. from second-sphere residues. The long-range electrostatic effects on reaction barriers are small for many systems. In the systems where large electrostatic effects have been reported, these lead to higher barriers. There is thus no evidence of any significant long-range electrostatic effects contributing to the catalytic efficiency of non-heme iron enzymes. However, the correct evaluation of electrostatic contributions is challenging, and the correlation between calculated residue contributions and the effects of mutation experiments is not very strong. The largest benefits of QM/MM models are thus the improved active-site geometries, rather than the calculation of accurate energies. Reported differences in mechanistic predictions between QM and QM/MM models can be explained by differences in hydrogen bonding patterns in and around the active site. Correctly constructed cluster models can give results with similar accuracy as those from multiscale models, but the latter reduces the risk of drawing the wrong mechanistic conclusions based on incorrect geometries and are preferable for all types of modeling, even when using very large QM parts. Journal of Biological Inorganic

“…18 Calculations have been performed using a private development version of Gaussian, with the capability to perform SCC-DFTB and ONIOM-PCM calculations. 14,19 Calculations where the PCM model is applied also to the model systems show limited effects (<1 kcal/mol) on the energy profiles for O-H bond dissociation in acetic acid and N-H bond dissociation in protonated 4-methylimidazole (bond lengths stretched to 2.0 Å). The reason for the small solvent effect is that the model calculations describe homolytic bond cleavage rather than charge separation.…”

Section: Computational Detailsmentioning

confidence: 99%

“…10,11 For large systems an alternative is the combination of a density functional as QM method with a semiempirical method or density-functional tight-binding (DFTB) as QM' method. [12][13][14] The flexibility of the ONIOM QM:QM' scheme makes it possible to choose an optimum combination of QM and QM' for a wide range of different phenomena.…”

Section: Introductionmentioning

confidence: 99%

Understanding cross‐boundary events in ONIOM QM:QM' calculations

2011

J Comput Chem

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QM:QM' models, where QM' is a fast molecular orbital method, offers advantages over standard QM:MM models, especially in the description of charge transfer and mutual polarization between layers. The ONIOM QM:QM' scheme also allows for reactions across the layer boundary, but the understanding of these events is limited. To explain the factors that affect cross-boundary events, a set of proton transfer processes, including the acylation reaction in serine protease, have been investigated. For reactions inside out, i.e., when a group breaks a bond in the high layer and forms a new bond with a group in the low layer, QM' methods that are overbinding relative to the QM method, e.g., Hartree-Fock vs B3LYP, can severely overestimate the exothermicity of the reaction. This might lead to artificial reactivity across the QM:QM' boundary, a phenomenon called model escape. The accuracy for reactions that occur outside in, i.e., when a group in the low layer forms a new bond with the high layer, is mainly determined by the QM' calculation. Cross-boundary reactions should generally be avoided in the present ONIOM scheme. Fortunately, a better understanding of these events makes it easy to design stable ONIOM QM:QM' models, e.g.,