Assessing AlphaFold AI’s Protease Enzyme Structure Prediction Accuracy
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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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".