Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules
Abstract via Crossref
Abstract 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.
Evidence snapshot note
This reference is cited by the validated MVA · BUB1B evidence snapshot (data as of 2026-10-17) as Toledano et al. — genome-scale PTC readthrough.
- Model in the source
- Human HEK293T landing-pad reporters; not endogenous BUB1B
- Where in the publication
- Supplementary Table 3; Extended Data Figure 4
- Cited by evidence records
- MVA-RT-002
The note describes how the snapshot used the source; it is not a summary of the publication's conclusions.
Identifiers and provenance
- DOI
10.1038/s41588-024-01878-5- PMID
39174735- PMCID
PMC11387191- Metadata from
- Crossref, Europe PMC, OpenAlex, fetched
- License (as declared by the source)
- https://creativecommons.org/licenses/by/4.0
- Open access
- Yes
- Added
- from the MVA evidence snapshot
In the library
- Collections
- MVA Hackathon 2026 — BUB1B
- Tags
- None yet
- Keywords from the source
- nonsense suppression, stop codon readthrough, premature stop codon, nonsense mutation, small molecule drugs, drug efficacy prediction, sequence context, tumor suppressor inactivation, genetic disorders, personalized treatment