Selecting DFT methods for use in optimizations of enzyme active sites: applications to ONIOM treatments of DNA glycosylases
Bibliographic record
Abstract
When using a hybrid methodology to treat an enzymatic reaction, many factors contribute to selecting the method for the high-level region, which can be complicated by the presence of dispersion-driven interactions such as π–π stacking. In addition, the proper treatment of the reaction center often requires a large number of heavy atoms to be included in the high-level region, precluding the use of ab initio methods such as MP2 as well as large basis sets, in the optimization step. In the present work, popular DFT methods were tested to identify an appropriate functional for treating the high-level region in ONIOM optimizations of reactions catalyzed by nonmetalloenzymes. Eight different DFT methods (B3LYP, B97-2, MPW1K, MPWB1K, BB1K, B1B95, M06-2X, and ωB97X-D) in combination with four double-ζ quality Pople basis sets were tested for their ability to optimize noncovalent interactions (hydrogen bonding and π–π) and characterize reactions (proton transfer, SN2 hydrolysis, and unimolecular cleavage). Although the primary focus of this study is accurate structure determination, energetics were also examined at both the optimization level of theory, and with triple-ζ quality basis set and select (M06-2X or ωB97X-D) methods. If dispersion-driven interactions exist within the active site, then MPWB1K/6-31G(d,p) or M06-2X/6-31+G(d,p) are recommended for the optimization step with subsequent triple-ζ quality single-point energies. However, since dispersion-corrected functionals (M06-2X and ωB97X-D) generally require diffuse functions to yield appropriate geometries, the possible size of the high-level region is greatly limited with these methods. In contrast, if the model is large enough to recover steric constraints on π–π interactions, then B3LYP with a small basis set performs comparatively well for the optimization step and is significantly less computationally expensive. Interestingly, the functionals that afford the best geometries often do not yield the best energetics, which emphasizes the importance of structural benchmark studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".