Biology subjects

Kuzuyama, T.

Publications and source records attributed to Kuzuyama, T..

4 recordsLinked to original sources

bioRxiv2025

Biosynthesis of Kaitocephalin: A Neuroprotective Natural Product Featuring a Peptide-Like yet Non-Peptidic Scaffold

Kaitocephalin (KCP, 1) is a neuroprotective natural product that acts as an antagonist of ionotropic glutamate receptors, making it a highly promising lead for drug discovery. It possesses a unique scaffold composed of three amino acids connected via C-C bonds, which appears peptide-like but is formed without peptide bonds. In this study, we identified the KCP biosynthetic gene cluster (kpb cluster) in the producing fungus Eupenicillium shearii through integrated genomic and transcriptomic analyses. LC-MS/MS profiling and chemical derivatization of E. shearii extracts led to the discovery of four novel pathway-related metabolites (2-5). In vitro enzymatic assays with 2(S)-dechlorokaito lactate (4) as a substrate enabled functional characterization of KpbI, KpbM, and KpbB involved in KCP formation. Among them, the dioxygenase KpbI was found to catalyze an unprecedented two-step oxidation to form the D-serine moiety. In addition, isotope tracing experiments provided new insights into the origin of the L-proline moiety. These findings establish a foundation for future studies aimed at elucidating the complete biosynthetic mechanism of KCP. Table of Contents graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=50 SRC="FIGDIR/small/683206v1_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@189618aorg.highwire.dtl.DTLVardef@62d5deorg.highwire.dtl.DTLVardef@c71341org.highwire.dtl.DTLVardef@1c14e59_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

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↗

bioRxiv2024

Tuning of ribosome levels mediated by RNase I and hibernating ribosomes

Protein biosynthesis is an energy-hungry intracellular process that requires the stringent regulation of ribosome abundance under environmental conditions. In response to stress, some active ribosomes are degraded while others, in bacteria, enter a hibernation state to protect against degradation. RNase I, a conserved T2 family ribonuclease in Escherichia coli, degrades ribosomal RNA to suppress biofilm formation, whereas it interacts with ribosomes. However, how and why RNase I binds to ribosomes remains elusive. Here, we show that hibernating ribosomes bind to RNase I and inhibit its activity, thereby promoting biofilm formation. We determined the cryo-electron microscopy structure of the hibernating ribosome complexed with RNase I. RNase I interacts with helix 41 of the 16S rRNA and the ribosomal protein uS14 in the head domain of the 30S subunit of the hibernating ribosome and positions its active centre away from helix 41, resulting in its catalytic inactivation. Hibernating ribosomes are protected from RNase I-mediated cleavage, and our in vivo and in vitro analyses revealed that RNase I targets dissociated large and small ribosomal subunits for rRNA degradation. These findings reveal a previously uncharacterized regulatory strategy that ribosomes modulate RNase I activity, ensuring both the preservation and timely degradation of ribosomes during environmental stress adaptation.

molecular biology↗

bioRxiv2023

Relationship Between Domain and Function of the Yeast RNase T2, Rny1p, Which Mediates rRNA Degradation upon Starvation

RNase T2 is ubiquitous across diverse organisms, playing essential roles despite its simple enzymatic activity. In Saccharomyces cerevisiae, RNase T2, known as Rny1p, localizes in vacuoles and mediates rRNA degradation during autophagy of ribosomes. In this study, we elucidated novel aspects of ribosome degradation mechanisms and the function of Rny1p. First, we discovered that most ribosomes are degraded by selective autophagy, where Rsa1p is the specific receptor of ribosomes to be degraded. Complex structure prediction suggested that Rsa1p also interacts with Atg8p. Furthermore, we observed that the accumulation of rRNA in vacuoles, due to the lack of Rny1p, leads to a decrease in bulk autophagic activity. This decrease in autophagic activity may explain the inability of Rny1p-deficient strains to adapt to starvation conditions. Second, our structural prediction and biochemical analyses indicate that a C-terminal extension, characteristic in fungal RNase T2 including Rny1p, is not necessary for rRNA degradation but for anchoring to the cell wall. Together with molecular phylogenetic analysis, a species-specific role of RNase T2 conferred by the C-terminal extension is suggested.

molecular biology↗