Experimental mapping of soluble protein domains using a hierarchical approach
Bibliographic record
Abstract
Exploring the function and 3D space of large multidomain protein targets often requires sophisticated experimentation to obtain the targets in a form suitable for structure determination. Screening methods capable of selecting well-expressed, soluble fragments from DNA libraries exist, but require the use of automation to maximize chances of picking a few good candidates. Here, we describe the use of an insertion dihydrofolate reductase (DHFR) vector to select in-frame fragments and a split-GFP assay technology to filter-out constructs that express insoluble protein fragments. With the incorporation of an IPCR step to create high density, focused sublibraries of fragments, this cost-effective method can be performed manually with no a priori knowledge of domain boundaries while permitting single amino acid resolution boundary mapping. We used it on the well-characterized p85α subunit of the phosphoinositide-3-kinase to demonstrate the robustness and efficiency of our methodology. We then successfully tested it onto the polyketide synthase PpsC from Mycobacterium tuberculosis, a potential drug target involved in the biosynthesis of complex lipids in the cell envelope. X-ray quality crystals from the acyl-transferase (AT), dehydratase (DH) and enoyl-reductase (ER) domains have been obtained.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".