Biology subjects

Herold, J.

Publications and source records attributed to Herold, J..

8 recordsLinked to original sources

bioRxiv2026

Conditional Generation And Inpainting Of Non-coding RNA Sequences With Masked Discrete Diffusion

Designing functional non-coding RNA (ncRNA) is fundamental to synthetic biology and RNA therapeutics, yet generative modelling for ncRNA has received far less attention than protein design. We present RNA-MDLM, a framework that extends Masked Discrete Language Models (MDLM) to the conditional generation and inpainting of ncRNA. We make two additions: first, conditioning on RNA-type representations from a pretrained RNA language model, and second, a modified classifier-free guidance scheme (Mod-CFG) that interpolates among conditional, unconditional, and random-sequence probabilities for better control. We also introduce REPAINT GAMES, a benchmark of seven structured masking tasks to probe a model's performance on sequence patterns, structural motifs, and base-pairing per RNA type. Our model is trained on 4.6 million ncRNA sequences spanning six evaluable classes. It produces sequences whose composition and folding statistics closely match natural RNAs. Through extensive ablation studies, we find that the embedding-conditioned model achieves the best balance of structural fidelity, biological novelty, and inpainting accuracy, and its class label steers generation far more strongly than a plain label baseline. We further show that a model trained on a smaller, class-balanced subset can appear more realistic mainly by copying abundant natural sequences rather than learning their rules. We also benchmark against a masked-diffusion model and a family-specific VAE on ribozyme families, and find that a type-conditioned model like ours and a per-family model are solving different tasks, which must be accounted for in a fair comparison. We will release the code and the trained models.

biophysics↗

bioRxiv2026

High immune receptor clonality in melanoma-draining lymph nodes associates with immune dysfunction and poor survival

Tumor-draining lymph nodes (tdLNs) are critical hubs of anti-tumor immunity but are also vulnerable to tumor-mediated immunosuppression. We analyzed T cell and B cell receptor (TCR/BCR) repertoires and transcriptomes from sentinel and non-sentinel lymph nodes of patients with melanoma from a historical pre-immune checkpoint inhibitor cohort (1994-2002) and an independent contemporary validation cohort (2022-2024). Melanoma-positive lymph nodes exhibited increased immune receptor clonality compared with tumor-free nodes. While average clonality showed no consistent association with outcome, the presence of extreme high-clonality outliers in individual lymph nodes was strongly associated with poor melanoma-specific survival. These outliers were characterized by a loss of lymphocyte-related genes and activation markers, an enrichment of melanocytic transcripts, and the suppression of immune signaling pathways, consistent with local immune dysfunction. Increased clonality was confined to lymph nodes and not observed in the peripheral blood. T cell responses to melanocyte differentiation antigens were infre-quently shared between lymph nodes and peripheral blood, highlighting immune compartmentalization.

immunology↗

bioRxiv2025

NucleicBERT: Deciphering the language of nucleic acids

The vast majority of the human genome comprises non-protein-coding regions whose structural and functional roles remain poorly understood. Many of these regions function through RNA, yet progress in deep learning for RNA has lagged behind proteins because most methods rely on abundant structural labels or evolutionary alignments, both sparse for RNA. To address these challenges, we developed NucleicBERT, a self-supervised masked-language model that learns contextual representations capturing local and distal dependencies without requiring alignments or evolutionary information. Explainable AI analysis reveals that the model clusters RNA types in latent space and attends to structural properties like secondary structure and tertiary contacts, effectively "rediscovering" RNA biology from sequence correlations alone. When fine-tuned for downstream structural and functional tasks, NucleicBERT requires only single sequences, yet surpasses current state-of-the-art RNA models. This alignment-free framework addresses the scarcity of annotated 3D RNA data while providing a rapid, computational complement to experimental techniques. By bridging abundant unlabeled primary sequence corpora with more scarce structural annotations, NucleicBERT advances RNA structure prediction and provides insights into the working of LLMs. NucleicBERT is available at https://github.com/KIT-MBS/NucleicBERT.

biophysics↗

bioRxiv2024

NucleoSeeker - Precision filtering of RNA databases to curate high-quality datasets

The structural prediction of biomolecules via computational methods complements the often involved wet-lab experiments. Un-like protein structure prediction, RNA structure prediction remains a significant challenge in bioinformatics, primarily due to the scarcity of annotated RNA structure data and its varying quality. Many methods have used this limited data to train deep learning models but redundancy, data leakage and bad data quality hampers their performance. In this work, we present NucleoSeeker, a tool designed to curate high-quality, tailored datasets from the Protein Data Bank (PDB) database. It is a unified framework that combines multiple tools and streamlines an otherwise complicated process of data curation. It offers multiple filters at structure, sequence and annotation levels, giving researchers full control over data curation. Further, we present several use cases. In particular, we demonstrate how NucleoSeeker allows the creation of a non-redundant RNA structure dataset to assess AlphaFold3s performance for RNA structure prediction. This demonstrates NucleoSeekers effectiveness in curating valuable non-redundant tailored datasets to both train novel and judge existing methods. NucleoSeeker is very easy to use, highly flexible and can significantly increase the quality of RNA structure datasets.

bioinformatics↗

bioRxiv2024

Leveraging Experimental Vasculature Data for High Resolution Brain Tumor Simulations

Cancer remains a leading cause of mortality. Multidisciplinary studies probe its complex pathology to increase treatment options. Computational modeling of tumor growth on high-performance computing resources offers microscopic insight into its progress and a valuable avenue for advancing our understanding. However, the effective initialization and parameterization of the underlying models require high-resolution data from real tissue structures. Here, we leveraged high performance computing resources and a massive dataset of a mouse brains entire vascular network. We processed these image stacks into detailed three-dimensional representations, identified brain regions of interest, and conducted a series of large-scale simulations to investigate how tumor growth is influenced by local vascular network characteristics. By simulating tumor growth with sub-cellular resolution, we can probe to which extent vessel density and vessel network length influence tumor growth. We determined that vessel density is the primary determinant of growth rate. Finally, our results allowed us to extrapolate tumor cell growth predictions for the entire mouse brain, highlighting the critical role of vascular topology in tumor progression. Such increasingly realistic simulations of cancer cells and their microenvironment enable researchers to increasingly bridge the gap between basic biology and clinical practice, ultimately supporting the development of more effective personalized cancer therapies.

cancer biology↗

bioRxiv2024

A mRNA Vaccine Encoding for a 60-mer G Glycoprotein Nanoparticle Elicits a Robust Neutralizing Antibodies Response Against the Nipah Virus

The Nipah virus is a zoonotic pathogen causing encephalitis and acute respiratory illness in humans with very high fatality rates. Here we report a novel messenger RNA vaccine that directly encodes for a nanoparticle displaying 60 head domains of the Nipah virus G (NiV G) glycoprotein that acts as a highly effective antigen. A vaccine encoding for the NiV G nanoparticle elicits high antibody titers against NiV G and a robust neutralizing antibody response with a pseudotyped Nipah virus system. We ultimately find that the proposed mRNA NiV G nanoparticle (mRNA NiV G-NP) provides a flexible platform for the development of vaccines that will likely be of great value in combatting future Nipah virus outbreaks.

immunology↗

bioRxiv2022

Development of a scoring function for comparing simulated and experimental tumor spheroids

Enormous progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), thus allowing cells to escape and invade alone or as multicellular collectives. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving internal organization of cells and external interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenging task. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel data-agnostic method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by our group. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable informing in silico spheroids based on their in vitro counterparts, and vice versa, thus enabling both basic and applied researchers to close the loop between modeling and experiments in cancer research. Author summaryCells within a tumor use various methods to escape and thereby invade into healthy parts of the body. These methods are studied experimentally by examining tumor spheroids, spherical aggregates of hundreds to thousands of individual cells. Such spheroids can also be simulated, and the comparison of simulation and experiment is desirable. Here, we present an analysis strategy for the comparison of tumor spheroids, a widely used workhorse of cancer research. Using this strategy, we aim to improve the collaborative potential between experimentalists and theorists.

cancer biology↗

bioRxiv2022

A mRNA vaccine encoding for a RBD 60-mer nanoparticle elicits neutralizing antibodies and protective immunity against the SARS-CoV-2 delta variant in transgenic K18-hACE2 mice

Two years into the COVID-19 pandemic there is still a need for vaccines to effectively control the spread of novel SARS-CoV-2 variants and associated cases of severe disease. Here we report a messenger RNA vaccine directly encoding for a nanoparticle displaying 60 receptor binding domains (RBDs) of SARS-CoV-2 that acts as a highly effective antigen. A construct encoding the RBD of the delta variant elicits robust neutralizing antibody response with neutralizing titers an order of magnitude above currently approved mRNA vaccines. The construct also provides protective immunity against the delta variant in a widely used transgenic mouse model. We ultimately find that the proposed mRNA RBD nanoparticle-based vaccine provides a flexible platform for rapid development and will likely be of great value in combatting current and future SARS-CoV-2 variants of concern.

immunology↗