FusionTarget: Computational framework for drug repurposing against modeled fusion protein structures from genomic...

Area of Science:

• Computational biology
• Genomics
• Drug discovery

Background:

• Fusion genes are crucial biomarkers and therapeutic targets in various cancers.
• Developing targeted therapeutics for fusion proteins is challenging due to limited structural and targeting information.

Purpose of the Study:

• To develop a computational pipeline, FusionTarget, for identifying small molecules that selectively target fusion proteins.
• To address the knowledge gap in fusion protein structures and targeting strategies for therapeutic development.

Main Methods:

• FusionTarget annotates genomic DNA breaks to RNA and protein sequences.
• It predicts 3D structures of fusion proteins and performs virtual screening and molecular dynamics simulations.
• The pipeline identifies selective small molecules by analyzing binding affinity between fusion and wild-type proteins across isoforms.

Main Results:

• The pipeline was applied to EWSR1::FLI1 in Ewing sarcoma and KMT2A::AFF1 in infant acute lymphoblastic leukemia.
• Cell assays confirmed increased sensitivity of cells expressing fusion genes to identified drugs.
• Key downstream genes were modulated by the identified drugs, validating the pipeline's predictions.

Conclusions:

• FusionTarget provides a robust computational framework for discovering novel therapeutics targeting fusion proteins.
• This approach facilitates the development of targeted therapies for cancers driven by specific fusion genes.