ProtDataTherm: A database for thermostability analysis and engineering of proteins
Bibliographic record
Abstract
Protein thermostability engineering is a powerful tool to improve resistance of proteins against high temperatures and thereafter broaden their applications. For efficient protein thermostability engineering, different thermostability-classified data sources including sequences and 3D structures are needed for different protein families. However, no data source is available providing such data easily. It is the first release of ProtDataTherm database for analysis and engineering of protein thermostability which contains more than 14 million protein sequences categorized based on their thermal stability and protein family. This database contains data needed for better understanding protein thermostability and stability engineering. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. This database is available at http://profiles.bs.ipm.ir/softwares/protdatatherm. As a proof of concept, the thermostability that improves mutations were suggested for one sample protein belonging to one of protein families with more than 20 mesophilic and thermophilic sequences and with known experimentally measured ΔT of mutations available within ProTherm database.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.024 |
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".