Determinants of Thermostability in Serine Hydroxymethyltransferase Identified by Principal Component Analysis
Bibliographic record
Abstract
Protein thermostability has received growing attention in recent years. Little is known about the determinants of thermal resistance in individual protein families. However, it is known that the mechanism is family-dependent and not identical for all proteins. We present a multivariate statistical analysis to find the determinants of thermostability in one protein family, the serine hydroxymethyltransferase family. Based on principal component analysis, we identified three amino acid fragments as the potential determinants of thermostability. The correlation coefficients between all the putative fragments and the protein thermostability were significant according to multivariable linear regression. Within the fragments, four critical amino acid positions were identified, and they indicated the contributions of Leu, Val, Lys, Asp, Glu, and Phe to thermostability. Moreover, we analyzed the insertions/deletions of amino acids in the sequence, which showed that thermophilic SHMTs tend to insert or delete residues in the C-terminal domain rather than the N-terminal domain. Our study provided a promising approach to perform a preliminary search for the determinants of thermophilic proteins. It could be extended to other protein families to explore their own strategies for adapting to high temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".