Proteome-wide analysis of human disease mutations in short linear motifs: neglected players in cancer?
Bibliographic record
Abstract
Disease mutations are traditionally thought to impair protein functionality by disrupting the folded globular structure of proteins. However, 22% of human disease mutations occur in natively unstructured segments of proteins known as intrinsically disordered regions (IDRs). This therefore implicates defective IDR functionality in various human diseases including cancer. The functionality of IDRs is partly attributable to short linear motifs (SLiMs), but it remains an open question how much defects in SLiMs contribute to human diseases. A proteome-wide comparison of the distribution of missense mutations from disease and non-disease mutation datasets revealed that, in IDRs, disease mutations are more likely to occur within SLiMs than neutral missense mutations. Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions. Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease. Furthermore, this in-depth analysis suggests that 'prostate cancer pathway' is particularly enriched for proteins with disease-related SLiMs. The contribution of mutations in SLiMs to disease may currently appear small when compared to mutations in globular domains. However, our analysis of mutations in predicted SLiMs suggests that this contribution might be more substantial. Therefore, when analysing the functional impact of mutations on proteins, SLiMs in proteins should not be neglected. Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".