Hydrophobin Fusions for High-Level Transient Protein Expression and Purification in<i>Nicotiana benthamiana</i>
Bibliographic record
Abstract
Insufficient accumulation levels of recombinant proteins in plants and the lack of efficient purification methods for recovering these valuable proteins have hindered the development of plant biotechnology applications. Hydrophobins are small and surface-active proteins derived from filamentous fungi that can be easily purified by a surfactant-based aqueous two-phase system. In this study, the hydrophobin HFBI sequence from Trichoderma reesei was fused to green fluorescent protein (GFP) and transiently expressed in Nicotiana benthamiana plants by Agrobacterium tumefaciens infiltration. The HFBI fusion significantly enhanced the accumulation of GFP, with the concentration of the fusion protein reaching 51% of total soluble protein, while also delaying necrosis of the infiltrated leaves. Furthermore, the endoplasmic reticulum-targeted GFP-HFBI fusion induced the formation of large novel protein bodies. A simple and scalable surfactant-based aqueous two-phase system was optimized to recover the HFBI fusion proteins from leaf extracts. The single-step phase separation was able to selectively recover up to 91% of the GFP-HFBI up to concentrations of 10 mg mL(-1). HFBI fusions increased the expression levels of plant-made recombinant proteins while also providing a simple means for their subsequent purification. This hydrophobin fusion technology, when combined with the speed and posttranslational modification capabilities of plants, enhances the value of transient plant-based expression systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".