Using computer-generated protein models to analyze mutations linked to Amelogenesis Imperfecta
Bibliographic record
Abstract
Amelogenesis Imperfecta (AI) is a disorder of tooth development caused by mutations in genes involved in several stages of tooth enamel formation. Few proteins involved in tooth development or developmental anomalies are explored in detail. Knowledge of 3D protein structure is essential to studying protein function. However, crystallized complete protein structures related to teeth and oral development are rare in the Protein Data Bank. Computational approaches for automated protein structure prediction have become a popular alternative for generating protein 3D structures. In this study, we aimed to explore the potential of using computer-generated protein models to analyze mutations linked to AI. We took a systematic approach to identify, screen, and analyze AI-linked protein variants. Proteins with AI-linked mutations were identified from the NCBI and OMIM databases, followed by screening of sequences for intrinsically disordered regions (IDRs). The iterative threading assembly refinement (I-TASSER) server was used to generate homology models for the wildtype and mutant proteins. PyMOL was used to analyze and compare the 3D structures of the proteins. Nineteen human genes with AI-associated mutations were identified from NCBI and OMIM. We identified multiple AI-associated protein variants with structural differences compared to their wildtype form. The current evidence aligns with several of the structural alterations identified in our study. Our findings suggest the potential of utilizing computer-generated protein models to investigate disease-associated mutations. However, careful consideration of models, templates, and alignments over the regions of interest is necessary to predict any potential structural impact of a disease-causing protein variant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".