Improved identification of outer membrane beta barrel proteins using primary sequence, predicted secondary structure, and evolutionary information
Why this work is in the frame
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Bibliographic record
Abstract
Membrane proteins (MPs) are difficult to identify in genomes and to crystallize, making it hard to determine their tertiary structures. MPs could be categorized into α-helical (AMP) and outer membrane proteins which mostly include beta barrel folds (OMBBs). The AMPs are relatively easy to predict from a protein sequence because they usually include several long membrane-spanning hydrophobic α-helices. The OMBBs play important roles in cell biology, they are targeted by multiple drugs, and they are more challenging to identify as they have shorter membrane-spanning regions which lack a folding pattern, that is, as consistent as in the case of the AMPs. Hence, accurate in silico methods for prediction of OMBBs from their primary sequences are needed. We present an accurate sequence-based predictor of OMBBs, called OMBBpred, which utilizes a Support Vector Machine classifier and a custom-designed set of 34 novel numerical descriptors derived from predicted secondary structures, hydrophobicity, and evolutionary information. Our method outperforms modern existing OMBB predictors and achieves accuracy of above 98% when tested on two existing benchmark datasets and 96% on a new large dataset. OMBBpred reduces the error rates of the second best method, depending on the dataset used, by between 13 and 65%, and generates predictions with high specificity of above 96%. Our solution is a useful tool for high-throughput discovery of the OMBBs on a genome scale and can be found at http://biomine.ece. ualberta.ca/OMBBpred/OMBBpred.htm.
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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 | 0.001 |
| 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 it