In Silico Comparison of Disulfide-Bearing and Disulfide-Free Phytases among Microorganisms
Bibliographic record
Abstract
Phytases are degrading enzymes that hydrolyze phytate (myo inositol hexa kis phosphate) to release a series of lower phosphate esters of myoinositol and orthophosphate. Phytase successfully used as an animal feed additive to increase the bioavailability of phosphate from phytic acid in the grain-based diets of poultry and swine. In order to investigate structural relationships between disulfide-bearing phytases and disulfide-free phytases, 9 phytases with resolved three-dimensional (3D) structure were retrieved as pdb and FASFA format from Protein Data Bank server and were analyzed using various tools and software. The results showed that 6 out of 9 phytases carry three or more disulfid bonds while the others lack any disulfide bonds. Our results also demonstrated that there is a remarkable correlation between the presence of disulfide bond and the number of amino acid in each phytase which means the larger enzymes contain three or more disulfide bonds whereas the enzymes containing less than 400 amino acids lack any disulfide bond. Additionally, in order to dig out the structure of phytases, some chemical and physical characteristics of phytases such as aliphatic index (AI), isoelectric pH (PI), amino acids percentage, molecular weights (MW) and 3D structure of phytases were analyzed. Results showed that phytases containing disulfide bonds have some identical characteristic including glycine percentage, AI, and 3D structure rather than disulfide-free phytases do. Moreover, evolutionary surveys by means of alignment studies and evaluations were conducted. Evolutionary analysis represented that phytases with disulfide bond most probably exhibited the same evolutionary course.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".