Holistic engineering of Cal-A lipase chain-length selectivity identifies triglyceride binding hot-spot
Bibliographic record
Abstract
Through the application of a region-focused saturation mutagenesis and randomization approach, protein engineering of the Cal-A enzyme was undertaken with the goal of conferring new triglyceride selectivity. Little is known about the mode of triglyceride binding to Cal-A. Engineering Cal-A thus requires a systemic approach. Targeted and randomized Cal-A libraries were created, recombined using the Golden Gate approach and screened to detect variants able to discriminate between long-chain (olive oil) and short-chain (tributyrin) triglyceride substrates using a high-throughput in vivo method to visualize hydrolytic activity. Discriminative variants were analyzed using an in-house script to identify predominant substitutions. This approach allowed identification of variants that exhibit strong discrimination for the hydrolysis of short-chain triglycerides and others that discriminate towards hydrolysis of long-chain triglycerides. A clear pattern emerged from the discriminative variants, identifying the 217-245 helix-loop-helix motif as being a hot-spot for triglyceride recognition. This was the consequence of introducing the entire mutational load in selected regions, without putting a strain on distal parts of the protein. Our results improve our understanding of the Cal-A lipase mode of action and selectivity. This holistic perspective to protein engineering, where parts of the gene are individually mutated and the impact evaluated in the context of the whole protein, can be applied to any protein scaffold.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".