Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.

ABSTRACT:

Motivation

Accurate prediction of single-guide RNA (sgRNA) activity is crucial for optimizing the CRISPR/Cas9 gene-editing system, as it directly influences the efficiency and accuracy of genome modifications. However, existing prediction methods mainly rely on large-scale experimental data of a single Cas9 variant to construct Cas9 protein (variants)-specific sgRNA activity prediction models, which limits their generalization ability and prediction performance across different Cas9 protein (variants), as well as their scalability to the continuously discovered new variants.

Results

In this study, we proposed PLM-CRISPR, a novel deep learning-based model that leverages protein language models to capture Cas9 protein (variants) representations for cross-variant sgRNA activity

SUBMITTER: Hou Y 

PROVIDER: S-EPMC12254127 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature