Functional characterization and discovery of modulators of SbMATE, the agronomically important aluminium tolerance transporter from <i>Sorghum bicolor</i>
Bibliographic record
Abstract
ABSTRACT About 50% of the world’s arable land is strongly acidic (soil pH < 5). The low pH of these soils solubilizes root-toxic ionic aluminium (Al 3+ ) species from clay minerals, driving the evolution of various counteractive adaptations in cultivated crops. The food crop Sorghum bicolor , for example, upregulates the membrane-embedded transporter protein SbMATE in its roots. SbMATE mediates efflux of the anionic form of the organic acid, citrate, into the soil rhizosphere, chelating Al 3+ ions and thereby imparting Al-resistance based on excluding Al +3 from the growing root tip. Here, we use electrophysiological, radiolabeled, and fluorescence-based transport assays in two heterologous expression systems to establish a broad substrate recognition profile of SbMATE, showing the transport of 14C - citrate anion, as well as the organic monovalent cation, ethidium, but not the divalent ethidium-derivative, propidium. The transport cycle is proton and/or sodium-driven, and shares certain molecular mechanisms with bacterial MATE-family transporters. We further complement our transport assays by directly measuring substrate binding to detergent-purified SbMATE protein. Finally, we use the functionally-folded, purified membrane protein as an antigen to discover high-affinity, native conformation-binding and transport function-altering nanobodies using an animal-free, mRNA/cDNA display technology. Our results demonstrate the utility of using Pichia pastoris as an efficient eukaryotic host to express large quantities of functional plant transporter proteins for in vitro characterization. The nanobody discovery approach is applicable to other low immunogenic plant proteins.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".