Leveraging protein language models and a scoring function for indel characterization and transfer learning.

ABSTRACT: Protein language models (PLMs) are increasingly used to assess the impact of genetic variants, achieving high accuracy and often outperforming traditional pathogenicity predictors. They enable zero-shot inference, making predictions without task-specific fine-tuning, though studying in-frame insertions and deletions (indels) remains challenging due to altered protein lengths and limited annotated datasets. Here, we present IndeLLM, a scoring approach for indel pathogenicity that accounts for sequence length differences. Our zero-shot method relies solely on sequence information, requires minimal computing resources, and achieves performance comparable to existing predictors. Building on this, we developed a Siamese network via transfer learning that outperformed all tested indel predictors

SUBMITTER: Gracia Carmona O 

PROVIDER: S-EPMC12921505 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature