Shotgun proteomics analysis of nanoparticle-synthesising <i>Desulfovibrio alaskensis</i> in response to platinum and palladium
Bibliographic record
Abstract
Abstract Platinum and palladium are much sought-after metals of global critical importance in terms of abundance and availability. At the nano-scale these metals are of even higher value due to their catalytic abilities for industrial applications. Desulfovibrio alaskensis is able to capture ionic forms of both of these metals, reduce them, and synthesize elemental nanoparticles. Despite this ability very little is known about the biological pathways involved in the formation of these nanoparticles. Proteomic analysis of D. alaskensis in response to platinum and palladium has highlighted those proteins involved in both the reductive pathways and the wider stress-response system. A core set of 13 proteins was found in both treatments and consisted of proteins involved in metal transport and reduction. There were also 7 proteins specific to either platinum or palladium. Over-expression of one of these platinum-specific genes, a NiFe hydrogenase small subunit (Dde_2137), resulted in the formation of larger nanoparticles. This study improves our understanding of the pathways involved in the metal resistance mechanism of Desulfovibrio and informs how we can tailor the bacterium for nanoparticle production, enhancing its application as a bioremediation tool and as way to capture contaminant metals from the environment. Importance Bacteria, in particularly D. alaskensis , represent a biological and greener way to capture high value metals such as platinum group metals from environmental and industrial waste streams. The recovery of these metals in nanoparticle forms adds extra value to this process as they can be used in a variety of different industrial applications as they have exceptional catalytic capabilities. D. alaskensis ability to do this, has been widely reported, though very little is understood about the underlying protein and genetic components. It is by understanding the biological basis of this capability that we can further improve and adapt this bacterium to be better at bioremediation and to control its ability to do so.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".