Biology subjects

Freeman, M. F.

Publications and source records attributed to Freeman, M. F..

4 recordsLinked to original sources

bioRxiv2026

Unconventional biocatalytic strategies orchestrate synthesis of the nucleoside analog sinefungin

Sinefungin is a potent nucleoside antimetabolite of S-adenosylmethionine (SAM), yet its biosynthesis has remained unclear for decades. Here we detail the identification and characterization of the complete sinefungin biosynthetic gene cluster (BGC) from Streptomyces incarnatus NRRL 8089. In vitro and in vivo analyses demonstrate that the defining carbon-carbon (C-C) bond is formed not by the long-hypothesized PLP-dependent process, but by a vitamin B12-dependent radical SAM enzyme. Using isotope-labeled cofactors and substrates, we provide evidence that the adenosyl group of sinefungin atypically originates from adenosylcobalamin via a homolytic SH2 substitution, establishing a rare instance where adenosylcobalamin is enzymatically consumed during the reaction. Furthermore, the pathway utilizes a cryptic phosphorylation-dephosphorylation strategy to control intermediate processing and substrate recognition. We also characterize two peptide aminoacyl-tRNA ligases (PEARLs) that append alanines onto the nucleoside scaffold using tRNA-activated amino acids. The PEARLs act directly on small molecules rather than macromolecular substrates, with one PEARL capable of iterative elongation. Finally, we leverage these enzymes in a reduced multi-enzyme cascade to biosynthesize sinefungin. Together, these findings redefine radical-mediated C-C bond formation and pearlin enzyme versatility, unlocking biocatalytic possibilities to produce amino acid-nucleoside conjugates. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/726688v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@10e48deorg.highwire.dtl.DTLVardef@d220ceorg.highwire.dtl.DTLVardef@167e60borg.highwire.dtl.DTLVardef@2fddec_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Computationally guided exploration of borosin biosynthetic strategies

Borosins are ribosomally synthesized and post-translationally modified peptides containing backbone -N-methylations. Identification of borosin precursor peptides is difficult because (1) there are no conserved sequence elements among borosin precursor peptides and (2) the biosynthetic gene clusters contain numerous domain architectures and peptide fusions. To tackle this problem, we updated the genome mining tool RODEO to automatically evaluate putative borosin BGCs and identify precursor peptides. Enabled by the new borosin module, we analyzed all borosin BGCs found in available sequence data and assigned precursor peptides to previously orphan borosin methyltransferases. Additionally, we bioinformatically predict and experimentally characterize a new fused borosin domain architecture, in which the modified core is N-terminal to the methyltransferase domain. Finally, we demonstrate that a borosin precursor peptide is the native substrate of shewasin A, a previously characterized pepsin-like aspartic peptidase whose native biological function was unknown.

bioinformatics↗

bioRxiv2021

Diverse Protein Architectures and α-N-Methylation Patterns Define Split Borosin RiPP Biosynthetic Gene Clusters

Borosins are ribosomally synthesized and post-translationally modified peptides (RiPPs) with -N-methylations installed on the peptide backbone that impart unique properties like proteolytic stability to these natural products. The borosin RiPP family was initially reported only in fungi until our recent discovery and characterization of a Type IV split borosin system in the metal-respiring bacterium Shewanella oneidensis. Here, we used hidden Markov models and sequence similarity networks to identify over 1,600 putative pathways that show split borosin biosynthetic gene clusters are widespread in bacteria. Noteworthy differences in precursor and -N-methyltransferase open reading frame sizes, architectures, and core peptide properties allow further subdivision of the borosin family into six additional discrete structural types, of which five have been validated in this study. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biochemistry↗

bioRxiv2021

TaxiBGC: a Taxonomy-guided Approach for the Identification of Experimentally Verified Microbial Biosynthetic Gene Clusters in Shotgun Metagenomic Data

Biosynthetic gene clusters (BGCs) in microbial genomes encode for bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read-assembly; predicting a narrow breadth of BGC classes; and not providing the SM product. To overcome these limitations, we developed TaxiBGC (Taxonomy-guided Identification of Biosynthetic Gene Clusters), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score: 0.56, mean PPV score: 0.80) compared to directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score: 0.49, mean PPV score: 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple gastrointestinal diseases, including Crohns disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCECurrently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally-oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding for bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared amongst multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.

bioinformatics↗