High-Throughput Mass Spectrometry Applied to Structural Genomics
Bibliographic record
Abstract
Mass spectrometry (MS) remains under-utilized for the analysis of expressed proteins because it is inaccessible to the non-specialist, and sample-turnaround from service labs is slow. Here, we describe 3.5 min Liquid-Chromatography (LC)-MS and 16 min LC-MSMS methods which are tailored to validation and characterization of recombinant proteins in a high throughput structural biology pipeline. We illustrate the type and scope of MS data typically obtained from a 96-well expression and purification test for both soluble and integral membrane proteins (IMPs), and describe their utility in the selection of constructs for scale-up structural work, leading to cost and efficiency savings. We propose that value of MS data lies in how quickly it becomes available and that this can fundamentally change the way in which it is used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".