Methods for enzyme library creation: Which one will you choose?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enzyme engineering allows to explore sequence diversity in search for new properties. The scientific literature is populated with methods to create enzyme libraries for engineering purposes, however, choosing a suitable method for the creation of mutant libraries can be daunting, in particular for the novices. Here, we address both novices and experts: how can one enter the arena of enzyme library design and what guidelines can advanced users apply to select strategies best suited to their purpose? Section I is dedicated to the novices and presents an overview of established and standard methods for library creation, as well as available commercial solutions. The expert will discover an up-to-date tool to freshen up their repertoire (Section I) and learn of the newest methods that are likely to become a mainstay (Section II). We focus primarily on in vitro methods, presenting the advantages of each method. Our ultimate aim is to offer a selection of methods/strategies that we believe to be most useful to the enzyme engineer, whether a first-timer or a seasoned user.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it