Characterization of a two-component kinase that initiates the bacterial catabolism of hydroxyphenylethanones
Bibliographic record
Abstract
The prodigious ability of bacteria to catabolize aromatic compounds has sparked considerable efforts to engineer bacteria to valorize lignin, an under-utilized component of biomass. Despite decades of study, key catabolic pathways and enzymes remain poorly characterized. We recently identified the hydroxyphenylethanone (Hpe) pathway, which enables Rhodococcus rhodochrous GD02 and other bacteria to catabolize 4-hydroxyacetophenone (HAP) and acetovanillone (AV), which are generated in the catalytic fractionation of lignin. Catabolism is initiated by a two-component, ATP-dependent dikinase, HpeHI, homologs of which are involved in the catabolism of other aromatic compounds. In biochemical studies, the kinase activity of HpeHI was highest at low ionic strength and low concentrations of Mn 2+ . HpeHI had highest apparent specificity for HAP and AV ( k cat / K M ≥ 250 mM -1 s -1 ) and had submicromolar K M values for these substrates, consistent with the enzyme acting as a scavenging system. The enzyme also transformed 4-hydroxybenzaldehyde, vanillin, acetosyringone, and phenol. A 1.8 Å crystal structure of HpeI revealed that it is homologous to the ATP-grasp domain of rifampin phosphotransferase (RPH) while an AlphaFold model of HpeH indicated that it is homologous to the swivel and rifampin-binding domains of RPH. Consistent with HpeHI using a similar mechanism where the swivel domain transits between the spatially distinct substrate-binding sites, substitution of the conserved His residue in HpeH abolished kinase activity. Moreover, the HpeH component alone catalyzed phosphotransfer from 4-phosphoacetophenone to AV. This study reveals a subfamily of small molecule dikinases that comprise two components, some of which are involved in aromatic compound catabolism.
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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) | 0.000 | 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 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".