A novel in-silico model explores LanM homologs among Hyphomicrobium spp
Bibliographic record
Abstract
Investigating microorganisms in metal-enriched environments holds the potential to revolutionize the sustainable recovery of critical metals such as lanthanides (Ln3+). We observe Hyphomicrobium spp. as part of a Fe2+/Mn2+-oxidizing consortia native to the ferruginous bottom waters of a Ln3+-enriched lake in Czechia. Notably, one species shows similarities to recently discovered bacteria expressing proteins with picomolar Ln3+ affinity. This finding was substantiated by developing an in-silico ionic competition model and recombinant expression of a homolog protein (Hm-LanM) from Hyphomicrobium methylovorum. Biochemical assays validate Hm-LanM preference for lighter Ln3+ ions (from lanthanum to gadolinium). This is comparable to established prototypes. Bioinformatics analyses further uncover additional H. methylovorum metabolic biomolecules in genomic proximity to Hm-LanM analogously dependent on Ln3+, including an outer membrane receptor that binds Ln3+-chelating siderophores. These combined observations underscore the remarkable strategy of Hyphomicrobium spp. for thriving in relatively Ln3+ enriched zones of metal-polluted environments. Microorganisms in a metal-enriched post-mining lake reveal the potential of Hyphomicrobium spp. for lanthanide recovery. A H. methylovorum protein shows strong affinity for light lanthanides,providing insights into microbial metal uptake strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".