Biology subjects

Geibel, C.

Publications and source records attributed to Geibel, C..

4 recordsLinked to original sources

bioRxiv2026

Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry

Tandem mass spectrometry (MS/MS) fragments molecules into smaller pieces, generating spectra composed of m/z values and intensities that encode structural information for molecular annotation. With increasing mass spectrometry data acquisition speeds, manual annotation from MS/MS lags far behind data generation and remains a bottleneck in metabolite annotation. Current computational methods, such as molecular networking, address this challenge by organizing similar structures into families of related compounds. However, they generally provide only similarity scores, offering weak actionable insights for structural annotation. To address this limitation, we present the Molecular Transformation Graph Edit Measure (MT-GEM), a distance metric that quantifies discrete structural transformations between molecules through graph edge removals that approximate structural modifications. Building on this metric, we developed an ensemble machine learning architecture, the Spectrum Transformation Edit Predictor (STEP), that builds upon TransExION and DREAMS to predict MT-GEM distances from MS/MS spectra. STEP achieves an average precision of 48.4% for identifying single structural transformations between MS/MS pairs, representing more than a tenfold improvement over state-of-the-art similarity metrics, including spectral entropy similarity (3.8%) and modified cosine (2.5%). On experimental human gut microbial community data, STEP identifies 3 times more single-transformation metabolite pairs than feature-based molecular networking at equivalent precision. In a discovery application, STEP highlights one drug metabolite and two new natural product analogs missed by modified cosine in feature-based molecular networking. By providing discrete transformation predictions rather than continuous similarity scores, MT-GEM and STEP enable hypothesis-driven metabolite annotation with testable structural modifications, which we envision will accelerate discovery of new molecules from MS/MS metabolomics datasets.

bioinformatics↗

bioRxiv2025

Identification of the negamycin split biosynthetic gene cluster in Kitasatospora purpeofusca ATCC21470

Negamycin is a ribosome-targeting antibiotic with activity against Gram-positive and Gram-negative bacteria including ESKAPE pathogens. Furthermore, it promotes premature stop codon readthrough. Its therapeutic potential is limited by low natural production and synthetic complexity. To enable scalable biosynthesis, we identified and characterized its genetic basis in Kitasatospora purpeofusca ATCC 21470. Two distant chromosomal regions, neg1 and neg2, were found to be essential. Deletion of neg1, involved in nitrite provision for N-N bond formation, reduced production to [~]10%, while deletion of neg2, which directs {beta}-lysine generation and scaffold assembly, abolished it completely. Isotope-labeling experiments confirmed nitrite incorporation. Transcriptomic and proteomic analyses further supported the involvement of both regions. The heterologous expression of neg1 along with the neg2 region in Streptomyces albidoflavus reconstituted negamycin biosynthesis, confirming the unusual involvement of two distant gene clusters in the biosynthesis, and provides a foundation for biotechnological production and further development of this promising antibiotic. SIGNIFICANCEThe rapid rise of antimicrobial resistance (AMR), particularly among Gram-negative ESKAPE pathogens, represents one of the most urgent global health threats. Despite this, the discovery and development of new antibiotics have stagnated. Addressing this challenge requires the exploration of natural products with novel mechanisms of action, alongside the development of scalable production strategies. Negamycin has emerged as a compelling candidate in this regard, characterized by an unusual mechanism of action and therapeutic potential extending beyond traditional antibacterial use. However, its development has been constrained by low production yields in the native producer. In this study, we identify and characterize the biosynthetic genes responsible for negamycin production, providing a foundation for pathway engineering, yield optimization, and the rational design of new analogs.

microbiology↗

bioRxiv2025

Native metabolomics identifies pteridines as CutA ligands and modulators of copper binding

CutA, a conserved protein across all domains of life, has long been linked to copper tolerance in Escherichia coli, though recent studies question this association. To clarify its function, we studied cutA knockout mutants from two phylogenetically distant species, E. coli and Synechococcus elongatus PCC 7942, using phenotyping combined with targeted and untargeted metabolomics. Native metabolomics of cell extracts revealed the lumazine dehydroxyxanthopterin B2, a previously uncharacterized pteridine, to bind CutA in both species. Based on these results, we identified other pteridines, including the essential cofactor tetrahydrobiopterin, as ligands of CutA proteins. In the presence of pterins, we observed higher affinity of CutA to copper ions. These findings, alongside the known role of pteridines as redox shuttles, suggest a previously unrecognized role for CutA in coordinating copper homeostasis and redox balance via pteridine metabolism. SignificanceWe identified the molecular class of pteridines as natural ligands of CutA, including the so far unknown lumazine dehydroxyxanthopterin B2. Pteridines are known redox shuttles involved in various cellular processes such as cofactors for redox enzymes. Our data showed increased copper binding to CutA in the presence of pteridines. Together, these results suggest that pteridines are physiological ligands of CutA that may modulate copper binding and redox homeostasis.

microbiology↗

bioRxiv2024

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the re-identification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder that leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes these information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

bioinformatics↗