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

Abstract

Premature termination codons (PTCs) cause [~]10-20% of Mendelian diseases and are the major mechanism of tumor suppressor gene inactivation in cancer. A general strategy to alleviate the effects of PTCs would be to promote translational readthrough. Nonsense suppression by small molecules has proven effective in diverse disease models, but translation into the clinic is hampered by ineffective readthrough of many PTCs. Here we directly tackle the challenge of defining drug efficacy by quantifying readthrough of [~]5,800 human pathogenic stop codons by 8 drugs. We find that different drugs promote readthrough of complementary subsets of PTCs defined by local sequence context. This allows us to build interpretable models that accurately predict drug-induced readthrough genome-wide. Accurate readthrough quantification and prediction will empower clinical trial design and the development of personalized nonsense suppression therapies.