Sequence-Only Based Prediction of β -Turn Location and Type Using Collocation of Amino Acid Pairs
Bibliographic record
Abstract
Development of accurate β-turn (beta-turn) type prediction methods would contribute towards the prediction of the tertiary protein structure and would provide useful insights/inputs for the fold recognition and drug design. Only one existing sequence-only method is available for the prediction of beta-turn types (for type I and II) for the entire protein chains, while the proposed method allows for prediction of type I, II, IV, VII, and non-specific (NS) beta-turns, filling in the gap. The proposed predictor, which is based solely on protein sequence, is shown to provide similar performance to other sequence-only methods for prediction of beta-turns and beta-turn types. The main advantage of the proposed method is simplicity and interpretability of the underlying model. We developed novel sequence-based features that allow identifying beta-turns types and differentiating them from non-beta-turns. The features, which are based on tetrapeptides (entire beta-turns) rather than a window centered over the predicted residues as in the case of recent competing methods, provide a more biologically sound model. They include 12 features based on collocation of amino acid pairs, focusing on amino acids (Gly, Asp, and Asn) that are known to be predisposed to form beta-turns. At the same time, our model also includes features that are geared towards exclusion of non-beta-turns, which are based on amino acids known to be strongly detrimental to formation of beta-turns (Met, Ile, Leu, and Val).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".