Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules.

Area of Science:

• Genetics
• Pharmacology
• Molecular Biology

Background:

• Premature termination codons (PTCs) are responsible for 10-20% of inherited diseases and cancer.
• Nonsense suppression via small molecules is a promising therapeutic strategy, but clinical translation is limited by variable drug efficacy.
• Developing effective therapies requires precise quantification and prediction of drug-induced readthrough at PTCs.

Purpose of the Study:

• To quantify the readthrough efficiency of eight drugs across approximately 5,800 human pathogenic stop codons.
• To develop predictive models for drug-induced readthrough based on local sequence context.
• To validate these models using endogenous stop codon readthrough.

Main Methods:

• Systematic quantification of drug-induced readthrough at a large set of pathogenic PTCs.
• Development of machine learning models to predict readthrough based on PTC sequence context.
• Validation of predictive models using endogenous stop codon readthrough experiments.

Main Results:

• Identified complementary subsets of PTCs responsive to different drugs based on sequence context.
• Built interpretable models that accurately predict drug-induced readthrough.
• Demonstrated the ability to predict readthrough at endogenous stop codons.

Conclusions:

• Drug efficacy for nonsense suppression varies significantly with PTC sequence context.
• Accurate readthrough prediction models can guide the development of personalized nonsense suppression therapies.
• This work will facilitate clinical trial design and advance the development of novel genetic therapies.