Biology subjects

Raubenolt, B.

Publications and source records attributed to Raubenolt, B..

4 recordsLinked to original sources

bioRxiv2026

Mechanistic prediction of fecal microbiota transplant engraftment and Clostridioides difficile colonization resistance with μODE

Shotgun metagenomics routinely resolves microbial community composition down to the strain level via metagenome-assembled genomes (MAGs), but translating these static catalogs into mechanistic, time-resolved understandings of ecosystem dynamics remains a critical computational bottleneck. Here, we present muODE, a fully automated framework that converts MAGs into dynamical simulations of community metabolism equipped with a composable, literature-grounded ecology layer, fundamentally designed to serve as a mechanistic hypothesis-testing engine. It employs dynamic flux balance analysis (dFBA) coupled to a shared extracellular pool and integrates a composable ecology layer capturing pH inhibition, time-varying antibiotic pharmacokinetics, bile-acid transformations, and spore dormancy. To evaluate the core engine, we cross-validated the dFBA machinery to machine precision against an independent reference implementation. We provide native export to the established COMETS engine for third-party verification. Benchmarking against two independent experimentally validated datasets confirmed that while monoculture growth is robustly captured, overflow secretion and directed community composition remain strictly bounded by the flux-routing limitations of max-biomass FBA. However, when applied to rate-modifying ecological feedback, muODE mechanistically and quantitatively reproduces bile-acid-mediated colonization resistance against Clostridioides difficile. With a single rate parameter calibrated so that baseline deoxycholate matched its measured physiological concentration, the framework reproduced the uncalibrated antibiotic-induced collapse of deoxycholate, with the calibrated inhibitor concentration coinciding with the independently set inhibitory threshold of the pathogen and driving a ~44x shift in the simulated pathogen-susceptibility ratio; bile-acid transformation is represented in the composable ecology layer rather than in the genome-scale stoichiometry. Finally, applying muODE end-to-end to a real longitudinal fecal microbiota transplant (FMT) yielding 139 de novo genome-scale metabolic models, the framework successfully recovered the clinical direction of treatment. By transparently diagnosing the structural competitive-exclusion bias that limits precise compositional prediction, muODE motivates the necessity of community-objective extensions while transitioning microbiome modeling from descriptive taxonomy toward testable, quantitatively-validated dynamic hypothesis testing, laying the essential mechanistic groundwork for the predictive engineering of safe, targeted ecological therapeutics. muODE is freely available at https://github.com/cumbof/muODE.

microbiology↗

bioRxiv2026

GalaxyMemMD: an end-to-end Galaxy toolset for reproducible membrane embedding, molecular dynamics simulation, and analysis of membrane proteins

Membrane proteins play crucial roles in cellular signaling, transport, and pharmacological regulation, yet their structural and functional characterization remains challenging due to experimental limitations. Molecular dynamics (MD) simulations have become indispensable for probing membrane protein behavior at atomic resolution; however, executing these simulations at scale remains a significant bottleneck. Existing tools excel at system preparation for individual cases, but none offer a complete, high-throughput pipeline spanning embedding, simulation, and analysis across multiple membrane compositions, and rarely integrate modern structure prediction and molecular docking frameworks. Here, we present GalaxyMemMD, a fully automated, open-source Galaxy framework that provides a direct, scalable alternative to existing preparation-only tools. GalaxyMemMD integrates every stage membrane embedding, system preparation, MD execution, and downstream analysis into a single reproducible pipeline. Leveraging Galaxy's HPC-ready job scheduling, GalaxyMemMD natively supports high-throughput parallel simulations across multiple membrane compositions and protein variants, including AlphaFold-derived structural inputs and downstream molecular docking workflows, without repeated manual setup. We validate GalaxyMemMD across six structurally diverse membrane proteins four GPCRs, one ion channel, and one beta-barrel demonstrating consistent system preparation, while the benchmark analysis shows stable embeddings, and reproducible trajectory-level production workflow. Across these benchmarks, GalaxyMemMD matches the setup quality of established tools, therefore serves as both a practical alternative and a capability extension for the membrane protein simulation community.

bioinformatics↗

bioRxiv2026

Structural Basis of M1 Muscarinic and H3 Histamine Receptor Inhibition in OPC Differentiation

Muscarinic and histamine receptors are neurotransmitter-binding proteins within the large family of G protein-coupled receptors (GPCRs) and are relevant to human health and disease, including multiple sclerosis (MS), a chronic immune-mediated inflammatory demyelinating disease of the central nervous system (CNS) with neurodegenerative components. MS affects approximately 1 in 333 people, and women are affected at roughly threefold higher rates than men. A major pathological feature of MS is demyelination with incomplete remyelination of axons in the CNS. Because oligodendrocyte progenitor cells (OPCs) can differentiate into mature oligodendrocytes that restore myelin, small molecules that promote OPC differentiation represent a potential therapeutic strategy. High-throughput screening identified 18 hit compounds with EC50 values below 0.2 M, including the lead compound CN045, which showed an EC50 of 40 nM in vitro. Cheminformatic and experimental target-identification studies implicated the M1 muscarinic receptor and the H3 histamine receptor as candidate targets. To interpret these findings, we performed docking, molecular dynamics simulations, and binding free-energy analyses on complexes involving CN045 and clemastine, a known antihistamine with antimuscarinic activity. The simulations support weaker and less stable binding of CN045 to H3 than to M1 and identify residue-level interactions that contribute to stability within the M1 binding pocket. Comparisons between CN045 and clemastine at M1 further suggest that the two ligands sample different local conformational ensembles, including differences in conserved microswitch behavior associated with active-like versus inactive-like receptor states. Together, these results provide a structural framework for understanding ligand-specific M1 engagement and may help guide future optimization of remyelination-promoting compounds.

biophysics↗

bioRxiv2025

Predicting the toxicity of chemical compounds via Hyperdimensional Computing

Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predominantly rely on intensive in vitro and in vivo assays, are frequently slow and expensive processes. Here, we introduce a novel application of Hyperdimensional Computing (HDC), an emerging computational paradigm inspired by the way the human brain works in encoding information, for the efficient classification of chemical compounds as either toxic or non-toxic. Our methodology employs Simplified Molecular Input Line Entry System (SMILES) representations of compounds, drawing data from the comprehensive Tox21 dataset. We delineate a pipeline wherein these chemical structures are encoded into high-dimensional binary vectors, which subsequently serve as the foundation for training and classification within the HDC framework. This approach leverages HDCs inherent advantages, including its resilience to noise, parallel processing capabilities, and efficacy in identifying intricate patterns. This work demonstrates the viability of HDC as a promising alternative for large-scale toxicity prediction, offering a computationally efficient and scalable solution. This research significantly contributes to the field of cheminformatics by validating HDCs potential in chemical property prediction, thereby facilitating accelerated identification of hazardous substances and mitigating the reliance on intensive laboratory experimentations.

bioinformatics↗