Function-Biased Choice of Additives for Optimization of Protein Crystallization: The Case of the Putative Thioesterase PA5185 from <i>Pseudomonas aeruginosa</i> PAO1
Bibliographic record
Abstract
The crystal structure of PA5185, a putative thioesterase from Pseudomonas aeruginosa strain PAO1, was solved using multi-wavelength anomalous diffraction to 2.4 Å. Analysis of the structure and information about the putative function of the protein were used to optimize crystallization conditions. The crystal growth was optimized by applying additives with chemical similarity to a fragment of a putative PA5185 substrate (CoA or its derivative). Using new crystallization conditions containing this function-biased set of additives, several new crystal forms were produced and structures of three of them (in three different space groups) were determined. One of the new crystal forms had an improved resolution limit of 1.9 Å, and another displayed an alternative conformation of the highly-conserved loop containing Asn26, which could play a physiological role. Surprisingly, none of the additives were ordered in the crystal structures. Application of function-biased additives could be used as a standard optimization protocol for producing improved diffraction, or new crystal forms, which may lead to better understanding of the biological functions of proteins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".