Assessing AlphaFold AI’s Protease Enzyme Structure Prediction Accuracy
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
This study analyzed AlphaFold AI’s ability in accurately predicting protease enzyme structures. AlphaFold uses machine learning, taking amino acid sequences and using physical and scientific knowledge of protein structures to generate a protein structure prediction. Past studies have comfirmed AlphaFold’s general abilities but have identified limitations in certain factors, like post translational modifications, ligands, and other environmental factors. However, there have not been studies assessing AlphaFold in predicting protease enzyme structures specifically. Quantitative data was collected using ex-post facto and correlational methods, which compared the RMSD score between AlphaFold and Protein Data Bank structures of the same protease enzyme. Furthermore, correlational trends were searched for between protein complexity and length with the RMSD score. 77% of the 30 protease enzymes assessed were found to be accurate, with more complex structures lowering in accuracy. Protein length was not a factor in AlphaFold’s prediction accuracy. By utilizing these findings, researchers in the pharmaceutical industry can consider the weak points of AlphaFold, conduct further studies identifying more factors that contribute to AlphaFold’s accuracy, and work on improving the program based on the results.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it