FusOn-pLM: a fusion oncoprotein-specific language model via adjusted rate masking

Sophia Vincoff, Shrey Goel, Kseniia Kholina, Rishab Pulugurta, Pranay Vure and Pranam Chatterjee ()
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Sophia Vincoff: Duke University
Shrey Goel: Duke University
Kseniia Kholina: Duke University
Rishab Pulugurta: Duke University
Pranay Vure: Duke University
Pranam Chatterjee: Duke University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Fusion oncoproteins, a class of chimeric proteins arising from chromosomal translocations, are major drivers of various pediatric cancers. These proteins are intrinsically disordered and lack druggable pockets, making them highly challenging therapeutic targets for both small molecule-based and structure-based approaches. Protein language models (pLMs) have recently emerged as powerful tools for capturing physicochemical and functional protein features but have yet to be trained on fusion oncoprotein sequences. We introduce FusOn-pLM, a fine-tuned pLM trained on a newly curated, comprehensive set of fusion oncoprotein sequences, FusOn-DB. Employing a unique cosine-scheduled masked language modeling strategy, FusOn-pLM dynamically adjusts masking rates (15%–40%) to optimize feature extraction and representation quality, surpassing baseline embeddings in fusion-specific tasks, including localization, puncta formation, and disorder prediction. FusOn-pLM uniquely predicts drug-resistant mutations, providing insights for therapeutic design that anticipates resistance mechanisms. In total, FusOn-pLM provides biologically relevant representations for advancing therapeutic discovery in fusion-driven cancers.

Date: 2025
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DOI: 10.1038/s41467-025-56745-6

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