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Published on: August 20, 2019
Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases
Mihaly Badonyi1, Joseph A Marsh1
1MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom.
Abstract:
Many dominant genetic disorders result from protein-altering mutations, acting primarily through dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF) mechanisms. Deciphering the mechanisms by which dominant diseases exert their effects is often experimentally challenging and resource intensive, but is essential for developing appropriate therapeutic approaches. Diseases that arise via a LOF mechanism are more amenable to be treated by conventional gene therapy, whereas DN and GOF mechanisms may require gene editing or targeting by small molecules. Moreover, pathogenic missense mutations that act via DN and GOF mechanisms are more difficult to identify than those that act via LOF using nearly all currently available variant effect predictors. Here, we introduce a tripartite statistical model made up of support vector machine binary classifiers trained to predict whether human protein coding genes are likely to be associated with DN, GOF, or LOF molecular disease mechanisms. We test the utility of the predictions by examining biologically and clinically meaningful properties known to be associated with the mechanisms. Our results strongly support that the models are able to generalise on unseen data and offer insight into the functional attributes of proteins associated with different mechanisms. We hope that our predictions will serve as a springboard for researchers studying novel variants and those of uncertain clinical significance, guiding variant interpretation strategies and experimental characterisation. Predictions for the human UniProt reference proteome are available at https://osf.io/z4dcp/.
Insights
This study introduces a novel statistical model to predict dominant genetic disorder mechanisms: dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF). The model aids in understanding disease pathways and guiding therapeutic strategies for genetic conditions.
Area of Science:
- Genetics
- Computational Biology
- Molecular Biology
Background:
- Dominant genetic disorders stem from protein-altering mutations with diverse mechanisms: dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF).
- Distinguishing these mechanisms is crucial for effective therapeutic development, as LOF disorders may respond to gene therapy, while DN and GOF require different approaches.
- Current variant effect predictors struggle to accurately identify pathogenic missense mutations associated with DN and GOF mechanisms.
Purpose of the Study:
- To develop a computational tool for predicting the molecular disease mechanisms (DN, GOF, LOF) of human protein-coding genes.
- To provide researchers with a resource to guide the interpretation of genetic variants and experimental characterization.
Main Methods:
- Development of a tripartite statistical model using support vector machine binary classifiers.
- Training the models to predict the likelihood of a gene being associated with DN, GOF, or LOF mechanisms.
- Validation of predictions by analyzing biologically and clinically relevant properties linked to each mechanism.
Main Results:
- The developed models demonstrate strong generalization capabilities on unseen data.
- Predictions offer insights into the functional attributes of proteins involved in different disease mechanisms.
- The study provides predictions for the human UniProt reference proteome, accessible online.
Conclusions:
- The statistical model effectively predicts dominant genetic disorder mechanisms (DN, GOF, LOF).
- This tool can significantly aid researchers in variant interpretation and experimental design for genetic diseases.
- The predictions serve as a valuable resource for understanding disease pathogenesis and developing targeted therapies.
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