Research Resource: EPSLiM: Ensemble Predictor for Short Linear Motifs in Nuclear Hormone Receptors
Bibliographic record
Abstract
Nuclear receptors (NRs) are a superfamily of transcription factors central to regulating many biological processes, including cell growth, death, metabolism, and immune responses. NR-mediated gene expression can be modulated by coactivators and corepressors through direct physical interaction or protein complexes with functional domains in NRs. One class of these domains includes short linear motifs (SLiMs), which facilitate protein-protein interactions, phosphorylation, and ligand binding primarily in the intrinsically disordered regions (IDRs) of proteins. Across all proteins, the number of known SLiMs is limited due to the difficulty in studying IDRs experimentally. Computational tools provide a systematic and data-driven approach for predicting functional motifs that can be used to prioritize experimental efforts. Accordingly, several tools have been developed based on sequence conservation or biophysical features; however, discrepancies in predictions make it difficult to determine the true candidate SLiMs. In this work, we present the ensemble predictor for short linear motifs (EPSLiM), a novel strategy to prioritize the residues that are most likely to be SLiMs in IDRs. EPSLiM applies a generalized linear model to integrate predictions from individual methodologies. We show that EPSLiM outperforms individual predictors, and we apply our method to NRs. The androgen receptor is an example with an N-terminal domain of 559 disordered amino acids that contains several validated SLiMs important for transcriptional activation. We use the androgen receptor to illustrate the predictive performance of EPSLiM and make the results of all human and mouse NRs publically available through the web service http://epslim.bwh.harvard.edu.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.024 | 0.011 |
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".