Predicted cellular interactors of the endogenous retrovirus-K protease enzyme
Why this work is in the frame
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Bibliographic record
Abstract
Retroviral proteases are essential enzymes for viral replication and drive changes within the cellular proteome. While several studies have demonstrated that protease (PR) enzymes from exogenous retroviruses cleave cellular proteins and modulate cellular signaling, the impact of PRs encoded by endogenous retroviruses within the human genome has been largely overlooked. One human symbiont called Endogenous retrovirus-K (ERVK) is pathologically associated with both neurological disease and cancers. Using a computational biology approach, we sought to characterize the ERVK PR interactome. The ERVK PR protein sequence was analyzed using the Eukaryotic Linear Motif (ELM) database and results compared to ELMs of other betaretroviral PRs and similar endogenated viral PRs. A list of putative ERVK PR cellular protein interactors was curated from the ELM list and submitted for STRING analysis to generate an ERVK PR interactome. Reactome analysis was used to identify key pathways potentially influenced by ERVK PR. Network analysis postulated that ERVK PR interacts at the apex of several ubiquitination pathways, as well as has a role in the DNA damage response, gene regulation, and intracellular trafficking. Among retroviral PRs, a predicted interaction with proliferating cell nuclear antigen (PCNA) was unique to ERVK PR. The most prominent disease-associated pathways identified were viral carcinogenesis and neurodegeneration. This strengthens the role of ERVK PR in these pathologies by putatively driving alterations in cellular signaling cascades via select protein-protein interactions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it