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

<h2>Summary</h2> Many fusion genes have been recognized as biomarkers and therapeutic targets. However, the lack of knowledge on protein structures and targeting approaches made it challenging to develop effective targeting therapeutics. To fill this, we developed a computational pipeline, FusionTarget, which annotates the genomic DNA breakage to RNA and protein sequences, predicts the 3D structures of fusion proteins, and performs comparative virtual screening, comparative molecular dynamics simulation, and quantitative analyses to identify the fusion protein-selective small molecules by selecting drugs with consistent high-fold binding affinity between fusion and wild-type proteins in multiple isoforms. We applied our pipeline to EWSR1::FLI1 in Ewing sarcoma and KMT2A::AFF1 in infant acute lymphoblastic leukemia. Further cell assay experiments confirmed that cells expressing individual fusion genes were more sensitive to the suggested drugs, and the key downstream genes were affected by our drugs. FusionTarget provides a unique foundation for developing therapeutics targeting fusion proteins.

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Publication Details

Journal
iScience
Published
2026-05-30
DOI
https://doi.org/10.1016/j.isci.2026.116076
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Juyoung Choi, Shalaka Lotlikar, Pora Kim, Kristopher W. Brannan et al.
iScience
Protein Structure and Dynamics
article

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

Juyoung Choi, Shalaka Lotlikar, Pora Kim, Kristopher W. Brannan, Yung-Hsing Huang, Himansu Kumar, Susan V. Tsang, Ji Young Song, Mi-Sook Lee, Andrea Toh, Yoon-La Choi
article en

Abstract

<h2>Summary</h2> Many fusion genes have been recognized as biomarkers and therapeutic targets. However, the lack of knowledge on protein structures and targeting approaches made it challenging to develop effective targeting therapeutics. To fill this, we developed a computational pipeline, FusionTarget, which annotates the genomic DNA breakage to RNA and protein sequences, predicts the 3D structures of fusion proteins, and performs comparative virtual screening, comparative molecular dynamics simulation, and quantitative analyses to identify the fusion protein-selective small molecules by selecting drugs with consistent high-fold binding affinity between fusion and wild-type proteins in multiple isoforms. We applied our pipeline to EWSR1::FLI1 in Ewing sarcoma and KMT2A::AFF1 in infant acute lymphoblastic leukemia. Further cell assay experiments confirmed that cells expressing individual fusion genes were more sensitive to the suggested drugs, and the key downstream genes were affected by our drugs. FusionTarget provides a unique foundation for developing therapeutics targeting fusion proteins.

iScienceVol. 29(6)
Houston Methodist (US), Samsung Medical Center (KR), The University of Texas Health Science Center (US), Sungkyunkwan University (KR), The University of Texas Health Science Center at Houston (US)
Cancer Prevention and Research Institute of Texas, Coalition for Epidemic Preparedness Innovations, National Research Foundation of Korea, National Institutes of Health
Zero hunger
Openalex Percentile: Top 8%
Protein Structure and Dynamics
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