Premature termination codons (PTCs) cause ~10–20% of inherited diseases and are a 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 the readthrough of ~5,800 human pathogenic stop codons by eight drugs. We find that different drugs promote the 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, and we validate these models by quantifying endogenous stop codon readthrough. Accurate readthrough quantification and prediction will empower clinical trial design and the development of personalized nonsense suppression therapies.

过早终止密码子 (PTC) 会导致约 10-20% 的遗传性疾病，并且是癌症中抑癌基因失活的主要机制。减轻 PTC 影响的一般策略是促进翻译阅读。小分子的无意义抑制已被证明在多种疾病模型中有效，但许多 PTC 的无效解读阻碍了临床转化。在这里，我们通过量化八种药物对约 5,800 个人类致病终止密码子的读取，直接应对定义药物功效的挑战。我们发现不同的药物促进了由局部序列上下文定义的 PTC 互补子集的通读。这使我们能够构建可解释的模型，准确预测药物诱导的全基因组通读，并且我们通过量化内源终止密码子通读来验证这些模型。准确的通读量化和预测将有助于临床试验设计和个性化无意义抑制疗法的开发。