How viral proteins bind short linear motifs and intrinsically disordered domains
Bibliographic record
Abstract
Viruses are the obligate intracellular parasites that exploit the host cellular machinery to replicate their genome. During the viral life cycle viruses manipulate the host cell through interactions with host proteins. Many of these protein-protein interactions are mediated through the recognition of host globular domains by short linear motifs (SLiMs), or longer intrinsically disordered domains (IDD), in the disordered regions of viral proteins. However, viruses also employ their own globular domains for binding to SLiMs and IDDs present in host proteins or virus proteins. In this review, we focus on the different strategies adopted by viruses to utilize proteins or protein domains for binding to the disordered regions of human or/and viral ligands. With a set of examples, we describe viral domains that bind human SLiMs. We also provide examples of viral proteins that bind to SLiMs, or IDDs, of viral proteins as a part of complex assembly and regulation of protein functions. The protein-protein interactions are often crucial for viral replication, and may thus offer possibilities for innovative inhibitor design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".