Master_Setup and Operation of Single Use (SUB) Bubble Column Reactors (BCR): Litre-Scale Expression of Recombinant Proteins for Structural Biology and Drug Design (SBDD). v1
Bibliographic record
Abstract
Structure-Based Drug Discovery (SBDD) is an approach to drug design which involves crystallising disease target proteins unbound and with candidate small-molecule binders, to observe and analyse the binding interaction, or to identify novel binders that can be developed into lead-like compounds. Obtaining sufficient protein quantity and crystal forms amenable to soaking with candidate binders remains a bottleneck. The Centre for Medicines Discovery’s (CMD) approach to this problem, is to design multiple constructs to explore protein expression and crystallisation behaviour. Protein crystallography (PX) has a high demand for protein quantities, a limiting factor in this workflow is the production of sufficient quantities of the multiple proteins for our experimental needs. This burden increases rapidly with the addition of each construct. Existing shake-flask workflows for protein expression do not scale adequately to meet these requirements. To address this bottleneck a novel, single-use Bubble-Column Bioreactor (suBCR) array was developed, that allows for parallel 1 L E. coli batch cultivation. This bioreactor array will be incorporated into a Gene-to-Product workflow aimed at increasing protein production efficiency by increasing throughput whilst reducing labour burden.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".