Biomedical subjects
Publications and source records attributed to Tae-Jin Oh.
Here, we report the complete genome sequence of Sphingobacterium sp. PAMC25397, isolated from Ny-Ålesund, Spitsbergen, Norway. The genome is 5.2 Mb in length with a GC content of 37%. This genome harbors biodegradation-related genes involved in benzoate degradation.
The present study presents the complete genomic sequence of a novel Bacillus zhangzhouensis PAMC22242 isolated from biofilm at Kongsfjorden dock, Svalbard, Norway. The genome size is 3,507,368, with a 41.5% GC content, including genes putatively associated with non-ribosomal peptide synthetase biosynthesis and metal-related transport systems.
Flavonoid O-methyltransferases (OMTs) catalyze the methylation of flavonoid hydroxyl groups, enhancing structural diversity and biological activity. In this study, we identified and characterized a novel Class I flavonoid OMT from the Antarctic bacterium Pedobacter sp. PAMC26386. Despite originating from a cold-adapted organism, the enzyme exhibited high catalytic activity at 55 °C and a strong preference for Co²⁺ as a cofactor. Sequence and phylogenetic analyses confirmed its classification as a flavonoid-specific OMT and revealed conserved motifs for S-adenosyl-L-methionine (SAM) binding and metal coordination. The enzyme accepted a wide range of flavonoid substrates, with quercetin and fisetin showing the highest activities. Kinetic analysis indicated greater substrate affinity for quercetin (Km = 36.55 µM) than for fisetin (Km = 49.41 µM). Whole-cell biotransformation using recombinant Escherichia coli C41 co-expressing the OMT and metK enabled efficient intracellular methylation, yielding 62.5 mg L⁻¹ of methylated quercetin and 55.5 mg L⁻¹ of 3'-O-methyl fisetin. The predicted methylation site at the 3'-hydroxyl of fisetin, determined by molecular docking, was confirmed by NMR spectroscopy. Notably, the previously reported 3'-O-methyl fisetin showed enhanced in vitro anticancer activity against mouse breast cancer cells and selectively improved antimycobacterial activity against Mycobacterium tuberculosis compared to the parent compound. To our knowledge, this study is among the first to report the enzymatic production of 3'-O-methyl fisetin using a polar microbial OMT that is optimally active at elevated temperatures in the presence of Co²⁺. The increased bioactivity compared to the parent compounds highlights the potential of this OMT as a biocatalyst for the sustainable production of pharmaceutically relevant methylated flavonoids.
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.