Peptide models. XXXIII. Extrapolation of low‐level Hartree–Fock data of peptide conformation to large basis set SCF, MP2, DFT, and CCSD(T) results. The Ramachandran surface of alanine dipeptide computed at various levels of theory
Bibliographic record
Abstract
At the dawn of the new millenium, new concepts are required for a more profound understanding of protein structures. Together with NMR and X-ray-based 3D-structure determinations in silico methods are now widely accepted. Homology-based modeling studies, molecular dynamics methods, and quantum mechanical approaches are more commonly used. Despite the steady and exponential increase in computational power, high level ab initio methods will not be in common use for studying the structure and dynamics of large peptides and proteins in the near future. We are presenting here a novel approach, in which low- and medium-level ab initio energy results are scaled, thus extrapolating to a higher level of information. This scaling is of special significance, because we observed previously on molecular properties such as energy, chemical shielding data, etc., determined at a higher theoretical level, do correlate better with experimental data, than those originating from lower theoretical treatments. The Ramachandran surface of an alanine dipeptide now determined at six different levels of theory [RHF and B3LYP 3-21G, 6-31+G(d) and 6-311++G(d,p)] serves as a suitable test. Minima, first-order critical points and partially optimized structures, determined at different levels of theory (SCF, DFT), were completed with high level energy calculations such as MP2, MP4D, and CCSD(T). For the first time three different CCSD(T) sets of energies were determined for all stable B3LYP/6-311++G(d,p) minima of an alanine dipeptide. From the simplest ab initio data (e.g., RHF/3-21G) to more complex results [CCSD(T)/6-311+G(d,p)//B3LYP/6-311++G(d,p)] all data sets were compared, analyzed in a comprehensive manner, and evaluated by means of statistics.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".