Related Endogenous Retrovirus-K Elements Harbor Distinct Protease Active Site Motifs
Bibliographic record
Abstract
Background: Endogenous retrovirus-K is a group of related genomic elements descending from retroviral infections in human ancestors. HML2 is the clade of these viruses which contains the most intact provirus copies. These elements can be transcribed and translated in healthy and diseased tissues, and some of them produce active retroviral enzymes, such as protease. Retroviral gene products, including protease, contribute to illness in exogenous retroviral infections. There are ongoing efforts to test anti-retroviral regimens against endogenous retroviruses. Herein, we examine the potential activity and diversity of human endogenous retrovirus-K proteases, and their potential for impact on immunity and human disease. Results: Sequences similar to the endogenous retrovirus-K HML2 protease and reverse transcriptase were identified in the human genome, classified by phylogenetic inference and compared to Repbase reference sequences. The topologies of trees inferred from protease and reverse transcriptase sequences were similar and agreed with the classification using reference sequences. Surprisingly, only 62/480 protease sequences identified by BLAST were classified as HML2; the remainder were classified as other HML groups, with the majority (216) classified as HML3. Variation in functionally significant protease motifs was explored, and two major active site variants were identified – the DTGAD variant is common in all groups, but the DTGVD motif appears limited to HML3, HML5, and HML6. Furthermore, distinct RNA expression patterns of protease variants are seen in disease states, such as amyotrophic lateral sclerosis, breast cancer and prostate cancer. Conclusions: Transcribed ERVK proteases exhibit a diversity which could impact immunity and inhibitor-based treatments, and these facets should be considered when designing therapeutic regimens.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".