FiGS-MoD: Feature-informed Gibbs Sampling Motif Discovery Algorithm for Mapping Human Signaling Networks
Bibliographic record
Abstract
Abstract Motivation Short linear motifs (SLiMs) are short sequence patterns that mediate transient protein-protein interactions, often within disordered regions of proteins. SLiMs play central roles in signaling, trafficking, and post-translational regulation, but their short length and low complexity make them difficult to identify both experimentally and computationally. Since the latest release of motif discovery tools like MEME Suite, the availability of protein-protein interaction data (e.g., BioGRID) has increased by more than fivefold, providing richer network contexts where SLiMs can be inferred from recurring patterns of interaction. Combined with recent advances in machine learning, this creates new opportunities for large-scale, high-resolution motif discovery. Results We present FiGS-MoD, a F eature- i nformed G ibbs S ampling Mo tif D iscovery algorithm with two key innovations: (i) incorporating biased sampling informed by residue-level features, including Protein Language Model (PLM) embeddings, AlphaFold2-derived disorder and solvent accessibility, and evolutionary conservation, and (ii) replacing the traditional position-specific scoring matrix (PSSM) with a Hidden Markov model (HMM) to accommodate insertions and deletions. We applied our algorithm to 12,765 sub-networks from the human interactome and provided 221,840 human SLiM predictions and quality scores as a public resource, along with the tool itself. Our method outperformed MEME in terms of recovering known motifs from the Eukaryotic Linear Motif (ELM) database and phosphosites from PhosphoGRID. Through three case studies, we further highlight the biological relevance of our results and the generalizability of the method to diverse motif classes. Availability and Implementation Source code and a predicted SLiM dataset using FiGS-MoD are freely available at https://github.com/Eric3939/FiGS-MoD
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".