Tertiary and quaternary structure prediction of full-length human p53 by comparative modelling with structural environment-based alignment method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the fundamental components for a wide range of proteomics research is to determine the 3D structure and properties of proteins. Access to precise and accurate protein models becomes very essential to predict the drug binding region or optimising the stability and selectivity of biologics. Due to biological and technical challenges of p53, the full-length 3D structure is unavailable for the scientific community; thus, there is a need to develop the 3D structure of p53, which is a key player in preventing cancer. Here, we model all the 393 amino acids to generate full-length 3D models of human p53 in both monomeric and tetrameric forms using computational approaches. The 3D model building involved homology-based modelling techniques combined with a refinement approach and use of structural environment-based alignment method for developing quaternary structure of human p53. Our results showed that 3D models are more reliable when iterative modelling was used and structural environment-based alignment method is well-suited to model the tetramer. These structures can be utilised to develop p53 mutants, virtual screening, design/develop small molecules or target-drug interaction studies.
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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 | 0.001 |
| 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 it