Deep learning tools predict variants in disordered regions with lower sensitivity
The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.
Authors
- Swantje Lenz (ORCID: https://orcid.org/0000-0002-8839-5371)
- Chi Fung Willis Chow (ORCID: https://orcid.org/0000-0001-9889-9664)
- Federica Luppino (ORCID: https://orcid.org/0000-0003-1702-2575)
- Ágnes Tóth-Petróczy (ORCID: https://orcid.org/0000-0002-0333-604X)
Institutions
- Center for Systems Biology Dresden (DE)
- Max Planck Institute of Molecular Cell Biology and Genetics (DE)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-04-12
- DOI
- https://doi.org/10.1186/s12864-025-11534-9
- Citations
- 11
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 19.99
Funders
- Deutsche Forschungsgemeinschaft
- Technische Universität Dresden