Methods for the Computational Prediction of Periplasmic Proteins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter addresses several different computational methods for the identification of periplasmic proteins from sequence information alone. The benefits, pitfalls, and performance of the methods are discussed, and an approach for the optimal computational identification of periplasmic proteins from a sequenced genome is presented. Recognizing that PSORT I could be significantly improved, the authors' group set out to develop a new method, PSORTb, for the prediction of protein subcellular localization in bacteria. Indeed, many of the other methods described in the chapter use ePSORTdb as a source of training and testing data. By parsing the remaining records into an easy-to-manipulate format such as tab-delimited text format, researchers can then identify periplasmic proteins by either manually reviewing each annotated localization site or extracting any records with an instance of the word “periplasm”. The former approach is slow, but has the advantage of allowing the researchers to incorporate their own expert knowledge into the review process. Of all the methods developed for signal peptide prediction, the suite of tools developed at the Technical University of Denmark has consistently been ranked as the best by several independent evaluations. These programs include SignalP, LipoP, and TatP, which are discussed individually. The chapter has presented an overview of a selection of methods for the computational identification of periplasmic proteins. While the analytical pipeline described in the chapter will identify a large proportion of periplasmic proteins with a moderate to high degree of confidence, the need still exists for improved localization prediction methods.
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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 | 0.000 |
| 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