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

Lundberg, Marcus; Sasakura, Yoko; Zheng, Gaige; Morokuma, Keiji

doi:10.1021/ct100029p

Cited by 50 publications

(53 citation statements)

References 75 publications

(206 reference statements)

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“…SCC‐DFTB has been adopted in the study of enzymatic systems to describe the lower level of the ONIOM method scheme (instead of using MM for the lower level as in typical QM/MM schemes), or in QM/MM calculations, where it describes the QM level of the studied systems, mostly in QM/MM molecular dynamics, coupled with umbrella sampling calculations . In the present system, the performance of SCC‐DFTB in the geometry of our system is similar to the GGA or LDA functionals, with a MUE of 0.09 Å (Supporting Information Table S2).…”

Section: Resultsmentioning

confidence: 99%

Benchmarking of density functionals for the kinetics and thermodynamics of the hydrolysis of glycosidic bonds catalyzed by glycosidases

Pereira

Ribeiro

Fernandes

et al. 2017

Int J of Quantum Chemistry

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In recent years, there has been an increased interest in understanding the enzymatic mechanism of glycosidases resorting mostly to DFT and DFT/MM calculations. However, the performance of density functionals (DFs) is well known to be system and property dependent. Trends drawn from general studies, despite important to evaluate the quality of the DFs and to pave the way for the development of new DFs, may be misleading when applied to a single specific system/property. To overcome this issue, we carried out a benchmarking study of 40 DFs applied to the geometry optimization and to the electronic barrier height (EBarrier) and electronic energy of reaction (ER) of prototypical glycosidase‐catalyzed reactions. Additionally, we report calculations with SCC‐DFTB and four semiempirical MO methods applied to the same problem. We have used a universal molecular model for retaining glycosidases, comprising only a 22‐atoms system that mimics the active site and substrate. High accuracy reference geometries and energies were calculated at the CCSD(T)/CBS//MP2/aug‐cc‐pVTZ level of theory. Most DFs reproduce the reference geometries extremely well, with mean unsigned errors (MUE) smaller than 0.05 Å for bond lengths and 3° for bond angles. Among the DFs, wB97X‐D, CAM‐B3LYP, B3P86, and PBE1PBE have the best performance in geometry optimizations (MUE = 0.02 Å). Conversely, semiempirical MO and SCC‐DFTB methods yielded less accurate geometries (MUE between 0.09 and 0.17 Å). The inclusion of D3 correction has a small, but still relevant, influence in the geometry predicted by some DFs. Regarding EBarrier, 11 DFs (MPW1B95, CAM‐B3LYP, M06 ‐ 2X, PBE1PBE, wB97X ‐ D, B1B95, BMK, MN12 – SX, M05, M06, and M11) presented errors below 1 kcal.mol−1, in relation to the reference energy. Most of these functionals belong to the family of hybrid functionals (H‐GGA, HH‐GGA, and HM‐GGA), which shows a positive influence of HF exchange in the determination of EBarrier. The inclusion of D3 correction has not affected significantly the EBarrier and ER. The use of geometries at the accurate but expensive MP2/aug‐cc‐pVTZ level of theory has a small, albeit not insignificant, influence in the EBarrier when compared with energies calculated with geometries determined with the DFs (usually a few tenths of kcal.mol−1, with exceptions). In general, semiempirical MO methods and DFTB are associated with larger errors in the determination of EBarrier, with unsigned errors from 6.9 to 24.7 kcal.mol−1.

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Section: Resultsmentioning

confidence: 99%

Benchmarking of density functionals for the kinetics and thermodynamics of the hydrolysis of glycosidic bonds catalyzed by glycosidases

Pereira

Ribeiro

Fernandes

et al. 2017

Int J of Quantum Chemistry

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“…[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). The alternative is a fully classical description of the electrostatic interactions, called mechanical embedding (ME).…”

Section: Low-level Treatment and Interactions Between Layersmentioning

confidence: 99%

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

Vedin

Lundberg

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

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“…Homolytic bond dissociation in the model system also explains the large difference between HF and BLYP. The model design of the serine protease system was taken from reference 20 .…”

Section: Computational Detailsmentioning

confidence: 99%

“…To isolate the effect of the potential proton transfer from His57 to Asp102, the actual ONIOM test system was designed with all groups except Asp102 in the model system, see Figure 11. Geometries were initially optimized using single-layer B3LYP/6-31G(d), 20 and all ONIOM calculations were performed at the B3LYP geometries. Deviations between methods are thus not affected by spurious optimizations to different local minima.…”

Section: Qm:qm' Model Of the Catalytic Triad In Serine Proteasementioning

confidence: 99%

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

Lundberg

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.,

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

Cited by 50 publications

References 75 publications

Benchmarking of density functionals for the kinetics and thermodynamics of the hydrolysis of glycosidic bonds catalyzed by glycosidases

Benchmarking of density functionals for the kinetics and thermodynamics of the hydrolysis of glycosidic bonds catalyzed by glycosidases

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

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

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