Dissecting the evolvability landscape of the CalB active site toward aromatic substrates
Bibliographic record
Abstract
A key event in the directed evolution of enzymes is the systematic use of mutagenesis and selection, a process that can give rise to mutant libraries containing millions of protein variants. To this day, the functional analysis and identification of active variants among such high numbers of mutational possibilities is not a trivial task. Here, we describe a combinatorial semi-rational approach to partly overcome this challenge and help design smaller and smarter mutant libraries. By adapting a liquid medium transesterification assay in organic solvent conditions with a combination of virtual docking, iterative saturation mutagenesis, and residue interaction network (RIN) analysis, we engineered lipase B from P. antarctica (CalB) to improve enzyme recognition and activity against the bulky aromatic substrates and flavoring agents methyl cinnamate and methyl salicylate. Substrate-imprinted docking was used to target active-site positions involved in enzyme-substrate and enzyme-product complexes, in addition to identifying 'hot spots' most likely to yield active variants. This iterative semi-rational design strategy allowed selection of CalB variants exhibiting increased activity in just two rounds of site-saturation mutagenesis. Beneficial replacements were observed by screening only 0.308% of the theoretical library size, illustrating how semi-rational approaches with targeted diversity can quickly facilitate the discovery of improved activity variants relevant to a number of biotechnological applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".