MARINE-EXPRESS: taking advantage of high throughput cloning and expression strategies for the post-genomic analysis of marine organisms
Bibliographic record
Abstract
BACKGROUND: The production of stable and soluble proteins is one of the most important steps prior to structural and functional studies of biological importance. We investigated the parallel production in a medium throughput strategy of genes coding for proteins from various marine organisms, using protocols that involved recombinatorial cloning, protein expression screening and batch purification. This strategy was applied in order to respond to the need for post-genomic validation of the recent success of a large number of marine genomic projects. Indeed, the upcoming challenge is to go beyond the bioinformatic data, since the bias introduced through the genomes of the so called model organisms leads to numerous proteins of unknown function in the still unexplored world of the oceanic organisms. RESULTS: We present here the results of expression tests for 192 targets using a 96-well plate format. Genes were PCR amplified and cloned in parallel into expression vectors pFO4 and pGEX-4T-1, in order to express proteins N-terminally fused to a six-histidine-tag and to a GST-tag, respectively. Small-scale expression and purification permitted isolation of 84 soluble proteins and 34 insoluble proteins, which could also be used in refolding assays. Selected examples of proteins expressed and purified to a larger scale are presented. CONCLUSIONS: The objective of this program was to get around the bottlenecks of soluble, active protein expression and crystallization for post-genomic validation of a number of proteins that come from various marine organisms. Multiplying the constructions, vectors and targets treated in parallel is important for the success of a medium throughput strategy and considerably increases the chances to get rapid access to pure and soluble protein samples, needed for the subsequent biochemical characterizations. Our set up of a medium throughput strategy applied to genes from marine organisms had a mean success rate of 44% soluble protein expression from marine bacteria, archaea as well as eukaryotic organisms. This success rate compares favorably with other protein screening projects, particularly for eukaryotic proteins. Several purified targets have already formed the base for experiments aimed at post-genomic validation.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".