Unassigned MURF1 of kinetoplastids codes for NADH dehydrogenase subunit 2
Bibliographic record
Abstract
BACKGROUND: In a previous study, we conducted a large-scale similarity-free function prediction of mitochondrion-encoded hypothetical proteins, by which the hypothetical gene murf1 (maxicircle unidentified reading frame 1) was assigned as nad2, encoding subunit 2 of NADH dehydrogenase (Complex I of the respiratory chain). This hypothetical gene occurs in the mitochondrial genome of kinetoplastids, a group of unicellular eukaryotes including the causative agents of African sleeping sickness and leishmaniasis. In the present study, we test this assignment by using bioinformatics methods that are highly sensitive in identifying remote homologs and confront the prediction with available biological knowledge. RESULTS: Comparison of MURF1 profile Hidden Markov Model (HMM) against function-known profile HMMs in Pfam, Panther and TIGR shows that MURF1 is a Complex I protein, but without specifying the exact subunit. Therefore, we constructed profile HMMs for each individual subunit, using all available sequences clustered at various identity thresholds. HMM-HMM comparison of these individual NADH subunits against MURF1 clearly identifies this hypothetical protein as NAD2. Further, we collected the relevant experimental information about kinetoplastids, which provides additional evidence in support of this prediction. CONCLUSION: Our in silico analyses provide convincing evidence for MURF1 being a highly divergent member of NAD2.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".