ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity
Bibliographic record
Abstract
UNLABELLED: ELASPIC is a novel ensemble machine-learning approach that predicts the effects of mutations on protein folding and protein-protein interactions. Here, we present the ELASPIC webserver, which makes the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in the Uniprot database, and allows all predicted results, including modeled wild-type and mutated structures, to be managed and viewed online and downloaded if needed. It is backed by a database which contains improved structural domain definitions, and a list of curated domain-domain interactions for all known proteins, as well as homology models of domains and domain-domain interactions for the human proteome. Homology models for proteins of other organisms are calculated on the fly, and mutations are evaluated within minutes once the homology model is available. AVAILABILITY AND IMPLEMENTATION: The ELASPIC webserver is available online at http://elaspic.kimlab.org CONTACT: pm.kim@utoronto.ca or pi@kimlab.orgSupplementary data: Supplementary data are available at Bioinformatics online.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.020 |
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".