ArticleNature communications2025
FusOn-pLM: a fusion oncoprotein-specific language model via adjusted rate masking.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- FusionTarget: Computational framework for drug repurposing against modeled fusion protein structures from genomic breakpoints.iScience · 2026Article
- Target sequence-conditioned design of peptide binders using masked language modeling.Nature biotechnology · 2026Article
- Functional protein design and enhancement with ontology reinforcement iteration.Nature communications · 2026Article
- UHRF1 as an epigenetic therapeutic target in Cancer.Molecular biology reports · 2026Review
- Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences.BMC bioinformatics · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- Entangled Schrödinger Bridge Matching.ArXiv · 2025Article
- PepTune:ArXiv · 2025Article
- Programmable protein stabilization with language model-derived peptide guides.Nature communications · 2025Article
- moPPIt:bioRxiv : the preprint server for biology · 2024Article
- Programmable Protein Stabilization with Language Model-Derived Peptide Guides.Research square · 2024Article
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6 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.