Biology subjects

Umemura, M.

Publications and source records attributed to Umemura, M..

3 recordsLinked to original sources

bioRxiv2026

Machine-learning-guided enzyme discovery and redox-system engineering for efficient production of the nylon monomer methyl 12-aminododecanoate in Escherichia coli

Methyl 12-aminododecanoate (ADAME) is a key precursor for Nylon 12 synthesis and an attractive target for sustainable microbial production. However, efficient biosynthesis of ADAME requires the coordinated oxidation and transamination of methyl dodecanoate (DAME), and identifying compatible enzymes for this multistep pathway remains challenging. In this study, we applied a machine learning-based bioinformatic screening approach to identify alternative enzymes for DAME-to-ADAME bioconversion. Among 18 selected distantly related AlkB homologs, AlkBGp01 from Alcanivorax sp. P2S70 showed activity toward DAME, producing 12-hydroxydodecanoic acid methyl ester (HDAME) and 12-oxododecanoic acid methyl ester (ODAME) despite low sequence identity to Pseudomonas putida AlkB. Further screening identified compatible redox partners, AlkG60 from the same species as AlkBGp01 and AlkTp04, whose combination with AlkBGp01 resulted in almost sixfold higher ODAME production than that achieved with P. putida AlkBFGJLT. ODAME production was further increased by more than threefold by conjugating these three newly identified proteins. The optimized single-plasmid system comprising conjugated AlkBGp01, AlkG60, and AlkTp04, together with AlaD and newly identified omega-transaminases EAV41574 produced 0.28 mM gDCW-1 ADAME, representing a 5.6-fold improvement over the previously reported P. putida AlkBFGJLT-based system with AlaD-CV2025. These results demonstrate that machine learning-guided enzyme discovery can identify functional distantly related homologs and compatible enzyme combinations for constructing efficient synthetic pathways. This study also highlights the importance of optimizing pathway architecture, including enzyme conjugation and plasmid configuration, for improving microbial production of bio-based monomer precursors.

bioengineering↗

bioRxiv2025

A novel transformer-based platform for the prediction and design of biosynthetic gene clusters for (un)natural products

Biosynthetic gene clusters (BGCs), comprising sets of functionally related genes responsible for synthesizing complex natural products, are a rich source of bioactive compounds with pharmaceutical potential. Here, we present a transformer-based framework that models functional domains as linguistic units to capture and predict their positional relationships within genomes. Using a RoBERTa architecture, we trained models on four progressively broader datasets: bacterial BGCs, Actinomycetes genomes, bacterial genomes, and bacterial plus fungal genomes. Evaluation using 2,492 experimentally-validated BGCs from the MIBiG database showed that more than 60% of true domains were ranked first and over 80% within the top 10 candidates. Our models also achieved classification accuracies exceeding 70% for major compound classes including polyketides (PKs) and terpenes. To explore model-guided BGC design, we compared predictions from the BGC-trained and genome-trained models using the BGC for the bacterial diterpenoid cyclooctatin as a case study. The genome-trained model uniquely predicted several domains absent from both the original BGC and the prediction by the BGC-trained model. Heterologous expression of one of those predicted domains in Streptomyces albus, together with the biosynthetic genes for cyclooctatin, yielded an unknown cyclooctatin derivative. This framework not only provides a novel BGC prediction method using machine learning but also facilitates rational design of artificial BGCs. Future integration of transcriptomic, protein structural, and phylogenetic data will enhance the models predictive and generative capabilities, supporting accelerated discovery and engineering of natural products. Author SummaryBGCs encode diverse natural products, including antibiotics and anticancer agents. Identifying and designing BGCs in microbial genomes is crucial for discovering new bioactive compounds. In this study, we developed a transformer-based deep learning model that treats protein domains as language-like tokens and learns how they are arranged in genomes. By training on both known BGCs and whole genomes, the model successfully predicts biologically plausible combinations of domains, including those absent in known BGCs. We experimentally validated one such prediction by expressing a newly identified gene alongside known cyclooctatin biosynthetic genes, confirming the production of an unknown cyclooctatin derivative. Our results demonstrate how language models can uncover hidden biosynthetic potential and offer a promising new AI tool for natural product discovery and synthetic biology.

bioinformatics↗

bioRxiv2020

Comparative genome and transcriptome analyses revealing interspecies variations in the expression of fungal biosynthetic gene clusters

Filamentous fungi produce various bioactive compounds that are biosynthesized by a set of proteins encoded in biosynthetic gene clusters (BGCs). For an unknown reason, large parts of the BGCs are transcriptionally silent under laboratory conditions, which has hampered the discovery of novel fungal compounds. The transcriptional regulation of fungal secondary metabolism is not fully understood from an evolutionary viewpoint. To address this issue, we conducted comparative genomic and transcriptomic analyses using five closely related species of the Aspergillus section Fumigati: Aspergillus fumigatus, Aspergillus lentulus, Aspergillus udagawae, Aspergillus pseudoviridinutans, and Neosartorya fischeri. From their genomes, 298 secondary metabolite (SM) core genes were identified, with 27.4% to 41.5% being unique to a species. Compared with the species-specific genes, a set of section-conserved SM core genes was expressed at a higher rate and greater magnitude, suggesting that their expression tendency is correlated with the BGC distribution pattern. However, the section-conserved BGCs showed diverse expression patterns across the Fumigati species. Thus, not all common BGCs across species appear to be regulated in an identical manner. A consensus motif was sought in the promoter region of each gene in the 15 section-conserved BGCs among the Fumigati species. A conserved motif was detected in only two BGCs including the gli cluster. The comparative transcriptomic and in silico analyses provided insights into how the fungal SM gene cluster diversified at a transcriptional level, in addition to genomic rearrangements and cluster gains and losses. This information increases our understanding of the evolutionary processes associated with fungal secondary metabolism. Author summaryFilamentous fungi provide a wide variety of bioactive compounds that contribute to public health. The ability of filamentous fungi to produce bioactive compounds has been underestimated, and fungal resources can be developed into new drugs. However, most biosynthetic genes encoding bioactive compounds are not expressed under laboratory conditions, which hampers the use of fungi in drug discovery. The mechanisms underlying silent metabolite production are poorly understood. Here, we attempted to show the diversity in fungal transcriptional regulation from an evolutionary viewpoint. To meet this goal, the secondary metabolisms, at genomic and transcriptomic levels, of the most phylogenetically closely related species in Aspergillus section Fumigati were compared. The conserved biosynthetic gene clusters across five Aspergillus species were identified. The expression levels of the well-conserved gene clusters tended to be more active than the species-specific, which were not well-conserved, gene clusters. Despite highly conserved genetic properties across the species, the expression patterns of the well-conserved gene clusters were diverse. These findings suggest an evolutionary diversification at the transcriptional level, in addition to genomic rearrangements and gains and losses, of the biosynthetic gene clusters. This study provides a foundation for understanding fungal secondary metabolism and the potential to produce diverse fungal-based chemicals.

genomics↗