Homology modeling and global computational mutagenesis of human myosin VIIa

Clinical Genetic Testing for Hearing Loss: Implications for Genetic Counseling and Gene-Based Therapies

Genetic factors contribute significantly to congenital hearing loss, with non-syndromic cases being more prevalent and genetically heterogeneous. Currently, 150 genes have been associated with non-syndromic hearing loss, and their identification has improved our understanding of auditory physiology and potential therapeutic targets. Hearing loss gene panels offer comprehensive genetic testing for hereditary hearing loss, and advancements in sequencing technology have made genetic testing more accessible and affordable. Currently, genetic panel tests available at a relatively lower cost are offered to patients who face financial barriers. In this study, clinical and audiometric data were collected from six pediatric patients who underwent genetic panel testing. Known pathogenic variants in MYO15A, GJB2, and USH2A were most likely to be causal of hearing loss. Novel pathogenic variants in the MYO7A and TECTA genes were also identified. Variable hearing phenotypes and inheritance patterns were observed amongst individuals with different pathogenic variants. The identification of these variants contributes to the continually expanding knowledge base on genetic hearing loss and lays the groundwork for personalized treatment options in the future.

Best templates outperform homology models in predicting the impact of mutations on protein stability

MOTIVATION

Prediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. In case, protein 3D structure is not available, one can predict the structure from protein sequence; however, the perspectives of ΔΔG predictions for predicted protein structures are unknown. The accuracy of using 3D structures of the best templates for the ΔΔG prediction is also unclear.

RESULTS

To investigate these questions, we used a representative set of seven diverse and accurate publicly available tools (FoldX, Eris, Rosetta, DDGun, ACDC-NN, ThermoNet and DynaMut) for stability change prediction combined with AlphaFold or I-Tasser for protein 3D structure prediction. We found that best templates perform consistently better than (or similar to) homology models for all ΔΔG predictors. Our findings imply using the best template structure for the prediction of protein stability change upon mutation if the protein 3D structure is not available.

AVAILABILITY AND IMPLEMENTATION

The data are available at https://github.com/ivankovlab/template-vs-model.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.