Functional Annotation of Putative Aminoglycoside Antibiotic Modifying Proteins in Mycobacterium tuberculosis H37Rv.
Bibliographic record
Abstract
The growing availability of sequences of bacterial genomes has revealed a number of open reading frames predicted by sequence alignment to encode antibiotic resistance proteins. The presence of these putative resistance genes within bacterial genomes raises important questions regarding potential reservoirs of resistance elements and their evolution. Here we examine four gene products encoding predicted aminoglycoside-aminocyclitol antibiotic modifying enzymes, two phosphotransferases and two acetyltransferases, derived from analysis of the genome sequence of Mycobacterium tuberculosis strain H37Rv with the goal of assigning biochemical function by purification of each protein and characterization of their ability to modify aminoglycoside antibiotics. Only one of these enzymes, the previously characterized aminoglycoside acetyltransferase AAC(2')-Ic, displayed compelling aminoglycoside modifying activity. While the putative phosphotransferase encoded by the Rv3225c gene did display low levels of aminoglycoside kinase activity, the predicted kinase encoded by the Rv3817 gene lacked any such activity. A potential aminoglycoside 6'-acetyltransferase, encoded by the Rv1347c gene, did not show antibiotic acylation activity but did demonstrate selective thioesterase activity with numerous acyl-CoAs. This activity, together with the genomic environment of the Rv1347c gene in a likely polyketide synthesis cluster, suggests a role for this protein in secondary metabolism and not in antibiotic modification. It was thus shown that only one of four putative aminoglycosides modifying enzymes derived from the whole genome sequencing of M. tuberculosis H37Rv showed sufficient predicted enzyme activity to be annotated as an aminoglycoside resistance element. This study demonstrates the necessity of biochemical annotation methods as a follow up to in silico sequence alignment-based methods of assigning gene product function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".