The assessment of density functionals for DNA–protein stacked and T-shaped complexes
Bibliographic record
Abstract
The present work uses 129 nucleobase – amino acid CCSD(T)/CBS stacking and T-shaped interaction energies as reference data to test the ability of various density functionals with double-zeta quality basis sets, as well as some semi-empirical and molecular mechanics methods, to accurately describe noncovalent DNA–protein π–π and π + –π interactions. The goal of this work is to identify methods that can be used in hybrid approaches (QM/MM, ONIOM) for large-scale modeling of enzymatic systems involving active-site (substrate) π–π contacts. Our results indicate that AMBER is a more appropriate choice for the lower-level method in hybrid techniques than popular semi-empirical methods (AM1, PM3), and suggest that AMBER accurately describes the π–π interactions found throughout DNA–protein complexes. The M06–2X and PBE-D density functionals were found to provide very promising descriptions of the 129 nucleobase – amino acid interaction energies, which suggests that these may be the most suitable methods for describing high-level regions. Therefore, M06–2X and PBE-D with both the 6–31G(d) and 6–31+G(d,p) basis sets were further examined through potential-energy surface scans to better understand how these techniques describe DNA–protein π–π interactions in both minimum and nonminimum regions of the potential-energy surfaces, which is critical information when modeling enzymatic reaction pathways. Our results suggest that studies of stacked nucleobase – amino acid systems should implement the PBE-D/6–31+G(d,p) method. However, if T-shaped contacts are involved and (or) smaller basis sets must be considered due to limitations in computational resources, then M06–2X/6–31G(d) provides an overall excellent description of both nucleobase – amino acid stacking and T-shaped interactions for a range of DNA–protein π–π and π + –π interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".