Synthesis of Degenerated Libraries of the <i>Ras</i>-Binding Domain of <i>Raf</i> and Rapid Selection of Fast-Folding and Stable Clones With the Dihydrofolate Reductase Protein Fragment Complementation Assay
Bibliographic record
Abstract
The protein-engineering field is mainly concerned with the design of novel enzyme activities or folds and with understanding the fundamental sequence determinants of protein folding and stability. Much effort has been put into the design of methods to generate and screen libraries of polypeptides. Screening for the ability of proteins to bind with high affinity and/or specificity is most often approached with phage display technologies. In this chapter, we present an alternative to phage display, performed totally in vivo, based on the dihydrofolate reductase (DHFR) protein-fragment complementation assay (PCA). We describe the application of the DHFR PCA to the selection of degenerated sequences of the ras-binding domain (RBD) of raf for correct folding and binding to ras. Our screening system allows for enrichment of the libraries for the best-behaving sequences through iterative competition experiments, without the discrete library screening and expansion steps that are necessary in in vitro approaches. Moreover, the selected clones can be processed rapidly to purification by Ni-nitrilotriacetic acid (NTA) affinity chromatography in 96-well plates. Our methods are particularly suitable for the designing and screening of libraries aimed at studying sequence folding and binding determinants. Finally, it can be adapted for library-against-library screening, thus, allowing for coevolution of interacting proteins simultaneously.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".