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Hyun Lee

Publications and source records attributed to Hyun Lee.

3 recordsLinked to original sources
PubMed2026

HRAS promotes mutant NRAS-driven transformation with codon and allele specificity.

Wild-type RAS family members determine the signaling and therapeutic response in cancers driven by mutant HRAS and KRAS because they activate alternate RAS effector pathways. Here, we found that the requirement for wild-type RAS to support mutant NRAS-driven transformation correlated with codon-specific differences in GTP hydrolysis. NRAS with mutations at either Gly12 (G12X) or Gly13 (G13X), which retained the GDP-GTP cycling function, had modest autonomous transforming potential. In contrast, NRAS with GTP-locking mutations at Gln61 (Q61X mutants) was uncoupled from receptor tyrosine kinase (RTK) input, rendering wild-type RAS an obligate partner for RTK-stimulated signaling and oncogenesis. In RASless cells expressing mutant NRAS, reintroduction of wild-type HRAS was sufficient to restore signaling and transformation. Global dependency mapping in human cancer cells revealed functional partitioning, wherein mutant NRAS promoted MAPK signaling and wild-type HRAS promoted PI3K-AKT survival signaling. Consequently, allele-specific or pan-RAS(ON) inhibitors synergized with inhibitors of proximal RTK signaling or of wild-type HRAS or KRAS to overcome this signaling plasticity. Pan-RAS(ON) and HRAS inhibition was synergistic for all NRAS mutants tested, with Q61X mutants showing greater sensitivity. These findings define the signaling partnership between mutant NRAS and wild-type HRAS as a targetable vulnerability and provide a biochemical blueprint for dual RAS inhibition in NRAS-mutated malignancies.

Humans↗
PubMed2026

Prediction of bacterial protein-compound interactions with only positive samples.

MOTIVATION: Prediction of Compound-Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields, including biocatalysis, drug discovery, and industrial processing. However, current CPI models cannot be applied for bacterial CPI prediction due to the lack of curated negative interaction samples. RESULTS: We propose a novel Positive-Unlabeled (PU) learning framework, named BIN-PU, to address this limitation. BIN-PU generates pseudo positive and negative labels from known positive interaction data, enabling effective training of deep learning models for CPI prediction. We also propose a weighted positive loss function that weights to truly positive samples. We have validated BIN-PU coupled with multiple CPI backbone models, comparing the performance with the existing PU models using bacterial cytochrome P450 (CYP) data. Extensive experiments demonstrate the superiority of BIN-PU over the benchmark models in predicting CPIs with only truly positive samples. Furthermore, we have validated BIN-PU on additional bacterial proteins obtained from literature review, human CYP datasets, and uncurated data for its reproducibility. We have also validated the CPI prediction for the uncurated CYP data with biological and biophysical experiments. BIN-PU represents a significant advancement in CPI prediction for bacterial proteins, opening new possibilities for improving predictive models in related biological interaction tasks. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/datax-lab/CYP.

Bacterial Proteins↗