Using computer-generated protein models to analyze mutations linked to Amelogenesis Imperfecta

TL;DR

This study uses computer-generated protein models to analyze mutations linked to a tooth development disorder called Amelogenesis Imperfecta.

Contribution

The study demonstrates the potential of computational modeling to investigate structural impacts of AI-linked protein mutations.

Findings

Nineteen human genes with AI-associated mutations were identified from NCBI and OMIM.

Multiple AI-associated protein variants showed structural differences compared to their wildtype forms.

Careful model selection and alignment are necessary to accurately predict structural impacts of mutations.

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.

Figures