Predicted Cellular Interactors of the Endogenous Retrovirus-K Integrase Enzyme
Bibliographic record
Abstract
Integrase (IN) enzymes are found in all retroviruses and are crucial in the retroviral integration process. Many studies have revealed how exogenous IN enzymes, such as the human immunodeficiency virus (HIV) IN, contribute to altered cellular function. However, the same consideration has not been given to viral IN originating from symbionts within our own DNA. Endogenous retrovirus-K (ERVK) is pathologically associated with neurological and inflammatory diseases along with several cancers. The ERVK IN interactome is unknown, and the question of how conserved the ERVK IN protein-protein interaction motifs are as compared to other retroviral integrases is addressed in this paper. The ERVK IN protein sequence was analyzed using the Eukaryotic Linear Motif (ELM) database, and the results are compared to ELMs of other betaretroviral INs and similar eukaryotic INs. A list of putative ERVK IN cellular protein interactors was curated from the ELM list and submitted for STRING analysis to generate an ERVK IN interactome. KEGG analysis was used to identify key pathways potentially influenced by ERVK IN. It was determined that the ERVK IN potentially interacts with cellular proteins involved in the DNA damage response (DDR), cell cycle, immunity, inflammation, cell signaling, selective autophagy, and intracellular trafficking. The most prominent pathway identified was viral carcinogenesis, in addition to select cancers, neurological diseases, and diabetic complications. This potentiates the role of ERVK IN in these pathologies via protein-protein interactions facilitating alterations in key disease pathways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".