Modeling β-Scission Reactions of Peptide Backbone Alkoxy Radicals: Backbone C−C Bond Fission
Bibliographic record
Abstract
To model the C-C β-scission reactions of backbone peptide alkoxy radicals, enthalpies and barriers for the fragmentation of four substituted alkoxy radicals have been calculated with a variety of ab initio molecular orbital theory and density functional theory procedures. The high-level methods examined include CBS-QB3, variants of the G3 family, and W1. Simpler methods include HF, MP2, QCISD, B3-LYP, BMK, and MPW1K with a range of basis sets. We find that good accuracy can be achieved with the G3(MP2)//B3-LYP and G3X(MP2)-RAD methods. Lower-cost methods producing reasonable results are single-point energy calculations with UB3-LYP/6-311+G(3df,2p), RB3-LYP/6-311+G(3df,2p), UBMK/6-311+G(3df,2p), and RBMK/6-311+G(3df,2p) on geometries optimized with UB3-LYP/6-31G(d) or UBMK/6-31G(d). Heats of formation at 0 K for the alkoxy radicals and their fragmentation products were also calculated. We predict ΔfH0 values for the alkoxy radicals of -71.4 ((•)OCH2CH [Formula: see text] O), -102.5 ((•)OCH(CH3)CH [Formula: see text] O), -176.6 ((•)OCH(CH3)C(NH2) [Formula: see text] O), and -264.6 ((•)OC(CH3)(NHCH [Formula: see text] O)CH [Formula: see text] O) kJ mol(-)(1). For the fragmentation products NH2C((•)) [Formula: see text] O and CH( [Formula: see text] O)NHC(CH3) [Formula: see text] O, we predict ΔfH0 values of -5.9 kJ mol(-)(1) and -352.8 kJ mol(-)(1).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".