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Published on: April 4, 2018
In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model
Eric Jonathan Maciel-Cruz1,2, Luis Eduardo Figuera-Villanueva1,2, Liliana Gómez-Flores-Ramos3
1Doctorado en Genética Humana, Instituto de Genética Humana "Dr. Enrique Corona Rivera", Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara (UdG), Guadalajara, Jalisco, México.
In-silico analysis offers a rapid, cost-free method for identifying potentially pathogenic single nucleotide variants. This study proposes a computational approach using the AURKA gene to predict variant pathogenicity, aiding further research.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- In-silico analysis presents a fast, simple, and cost-free approach for identifying potentially pathogenic single nucleotide variants.
- Predicting variant pathogenicity is crucial for understanding genetic diseases.
Purpose of the Study:
- To propose a simple, fast, and cost-free method for predicting variant pathogenicity using in-silico (IS) tools.
- To utilize the AURKA gene as a model for developing and validating this predictive methodology.
Main Methods:
- Computational analysis of 209 AURKA variants from the Ensembl database.
- Prediction of protein models and variant pathogenicity using various bioinformatic tools.
- Comparison of predicted results with VarSome website and American College of Medical Genetics (ACMG) classification.
Main Results:
- Out of 209 analyzed variants, 16 were predicted as pathogenic, with 13 located in the catalytic domain.
- Proline and Glycine substitutions were the most frequent pathogenic changes, often causing amino acid size and hydrophobicity modifications.
- Bioinformatic tools predicted functional impacts including altered protein expression, molecular interactions, and structural changes.
Conclusions:
- The proposed in-silico method enables rapid and cost-free screening of variants for pathogenic potential.
- This approach facilitates the identification of variants for subsequent association and functional studies.
- The methodology shows promise for prioritizing variants of interest in genetic research.
