On the flexibility of β‐peptides
Bibliographic record
Abstract
The full conformational space was explored for an achiral and two chiral beta-peptide models: namely For-beta-Ala-NH2, For-beta-Abu-NH2, and For-beta-Aib-NH2. Stability and conformational properties of all three model systems were computed at different levels of theory: RHF/3-21G, B3LYP/6-311++G(d,p)//RHF/3-21G, B3LYP/6-311++G(d,p), MP2//B3LYP/6-311++G(d,p), CCSD//B3LYP/6-311++G(d,p), and CCSD(T)//B3LYP/6-311++G(d,p). In addition, ab initio E = E(phi, micro, psi) potential energy hypersurfaces of all three models were determined, and their topologies were analyzed to determine the inherent flexibility properties of these beta-peptide models. Fewer points were found and assigned than expected on the basis of Multidimensional Conformational Analysis (MDCA). Furthermore, it has been demonstrated, that the four-dimensional surface, E = E(phi, mu, psi), can be reduced into a three-dimensional one: E = E[phi, f(phi), psi]. This reduction of dimensionality of freedom of motion suggests that beta-peptides are less flexible than one would have thought. This agrees with experimental data published on the conformational properties of peptides composed of beta-amino acid residues.
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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) | 0.000 | 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) | 0.001 | 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".