Biology subjects

Barth, P.

Publications and source records attributed to Barth, P..

14 recordsLinked to original sources

bioRxiv2026

Efficiency of RNAi based gene silencing in fungi - a review and meta-analysis

RNA interference (RNAi) shows great potential to protect crops against fungal diseases, yet reported protection efficiencies vary greatly, and our understanding of the factors responsible for this variance remains limited. In this meta-analysis, we evaluated 89 studies that compare the efficiency of host-induced gene silencing (HIGS) and spray-induced gene silencing (SIGS) in controlling fungal diseases, focusing on biotrophic, hemibiotrophic, and necrotrophic fungi, the use of formulations, and the dsRNA design as explanatory factors for differences between reported efficiency values. Our results indicate that SIGS is slightly more effective, particularly in biotrophs. Surprisingly, SIGS studies using formulations did not outperform those applying naked dsRNA. We also assessed parameters of RNA design. Differences in dsRNA length and the number of constructs, and number of targets showed no consistent significant effect on resistance in either HIGS or SIGS. Interestingly, however, HIGS studies reported significantly higher efficiency when targeting genes closer to the 3 end and SIGS when targeting genes closer to the 5 end. We discuss potential reasons for the reported patterns, such as variability in dsRNA uptake mechanisms, intercellular trafficking and Dicer processing, and conclude that more research is needed to understand the biological mechanisms determining RNAi efficiency for fungal control.

plant biology↗

bioRxiv2026

Physically Grounded Generative Modeling of All-Atom Biomolecular Dynamics

Predicting the kinetic pathways of biomolecular systems at all-atom resolution is crucial for understanding protein function and drug efficacy, yet this task is hindered by the immense computational cost of conventional molecular dynamics (MD) simulations. While deep learning has revolutionized static structure prediction and equilibrium ensemble sampling, simulating the kinetics of conformational transitions remains a critical challenge. We introduce BioKinema, a physically grounded generative model that predicts continuous-time, all-atom biomolecular trajectories at a fraction of the cost of traditional simulations. In particular, BioKinema utilizes a spatial-temporal diffusion architecture motivated by the exponential decay of correlations characteristic of Langevin dynamics, and is explicitly trained to generate trajectories with the correct kinetics and thermodynamics. It employs a hierarchical forecasting-and-interpolation strategy to overcome the error accumulation that often plagues long-horizon generation. Through extensive validation, we demonstrate that BioKinema generates physically stable and dynamically accurate trajectories suitable for rigorous downstream analysis. For protein systems, it reproduces the equilibrium thermodynamics of the conformational ensemble and the underlying kinetics. For protein-ligand complexes, it successfully elucidates mechanisms such as ligand-driven conformational changes and allosteric interactions. Furthermore, BioKinema leverages enhanced sampling data to predict rare kinetic events, emerging as a powerful tool for estimating ligand unbinding pathways. Collectively, these results establish BioKinema as a computationally efficient complement to MD simulations that bridges the gap between static structure and dynamic function, enabling high-throughput exploration of the kinetic landscape for structural biology and drug discovery.

bioinformatics↗

bioRxiv2025

Molecular characterization of an adhesion GPCR signal transduction

Key cellular processes rely on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein-coupled receptors (aGPCRs). While recent studies support aGPCR activation via shedding of the extracellular GAIN domain, shedding-independent signaling mechanisms have also been observed. However, the molecular basis underlying these distinct activation modes remains poorly understood. Here, we integrate single-molecule force spectroscopy, molecular dynamics simulations, and cell-based assays to elucidate the structural and dynamic mechanisms of ADGRG1 mechanotransduction. We show that shear stress induces distinct deformation pathways in the isolated GAIN domain, promoting tethered agonist (TA) exposure through loop rearrangements prior to domain shedding. In the full-length receptor, defined GAIN orientations and specific loop contacts with the 7-transmembrane (7TM) core enable allosteric TA engagement and signaling in the absence of GAIN dissociation. The directionality of the applied force dictates the activation pathway, favoring either GAIN shedding or intact GAIN-7TM coupling. These mechanisms align with both the basal activity and collagen-enhanced signaling of ADGRG1. Using deep learning-guided design, we engineered GAIN variants with tailored mechanical sensitivity, validating our model through predictable shifts in constitutive and ligand-induced signaling. Together, our findings establish a unified framework for aGPCR activation governed by GAIN dynamics and orientation, bridging mechanical and allosteric models of receptor function and providing new strategies for engineering mechanosensitive receptors and precision therapeutics.

biophysics↗

bioRxiv2025

Computational design of allosteric pathways reprograms ligand-selective GPCR signaling

G-protein-coupled receptors (GPCRs) constitute the largest family of signaling receptors and drug targets. However, understanding how variations in receptor sequence, ligand chemical structure, and binding impact signaling functions remains a challenge, hindering drug discovery. Here, we developed a computational protein structure and dynamics approach to infer and design GPCR responses to multiple ligands. We created 32 dopamine D1 and D2 receptor variants with widely reprogrammed agonist-induced signal transductions. Subtle natural and designed receptor sequence variations, predicted to alter specific structural and dynamic mechanisms of ligand responses, profoundly impacted ligand potency and efficacy in agreement with our calculations. Our study provides a rational blueprint for computing the effect of sequence polymorphisms on ligand-selective protein signaling and paves the way for advancements in pharmacogenomics, drug selectivity, and the design of signaling receptors from first principles.

biochemistry↗

bioRxiv2025

Deep learning-based joint sequence-structure de novo membrane protein design

Deep learning has revolutionized soluble protein design, yet de novo transmembrane (TM) protein engineering remains hindered by scarce structural data, complex membrane-specific interactions and conformational dynamics. We developed TMDiffusion (TMDF), a joint all-heavy-atom sequence-structure diffusion model trained to capture the full interaction diversity of natural TM proteins, including weak and polar contact networks. TMDF designs diverse TM architectures--associating domains, inhibitors, and conformational switches--in a single step, achieving >70% experimental success. A crystal structure of designed proteins matches predictions with atomic accuracy. Leveraging TMDF, we built synthetic single-pass receptors whose de novo TM domains toggle between conformations, enabling precise control of signalling outputs consistent with predicted equilibria. These results show that membrane-adapted DL models can accurately encode and program TM association energetics and conformations. TMDF establishes a general framework for bottom-up design of TM proteins with programmable functions, advancing both mechanistic studies of membrane proteins and development of next-generation therapeutics.

biophysics↗

bioRxiv2024

Minimal integrating shuttle vectors for Saccharomyces cerevisiae depleted of restriction sites outside the polylinker region

Many plasmids harbor unnecessary elements that complicate or hinder cloning tasks such as inserting one gene into another for protein domain grafting. In particular, restriction sites may be present in the backbone outside the polylinker region (multiple cloning site; MCS) and thus unavailable for use, and the overall length of a plasmid correlates with poorer ligation efficiency. To address these concerns, there has been a growing interest in minimal plasmids. Here, we describe the design and validation of a collection of six minimal integrating shuttle vectors for genetic manipulation in Saccharomyces cerevisiae. We constructed the plasmids using de novo gene synthesis and consisting only of a yeast selection marker (HIS3, TRP1, LEU2, URA3, natMX6, or KanMX), a bacterial selection marker (Ampicillin resistance), an origin of replication (ORI), and the MCS flanked by M13 forward and reverse sequences. We use truncated variants of these elements where available and eliminated all other sequences typically found in plasmids. The MCS consists of ten unique restriction sites. To our knowledge, at sizes ranging from approximately 2.6 kb to 3.5 kb, these are the smallest shuttle vectors described for yeast. Further, we removed common restriction sites in the open reading frames (ORFs) and terminators, freeing up approximately 30 cut sites in each plasmid. We named our pLS series in accordance with the well-known pRS vectors, which are on average 63% larger: pLS403 (HIS3), pLS404 (TRP1), pLS405 (LEU2), pLS406 (URA3), pLS408 (natMX6), and pLS410 (KanMX). These minimal vector backbones open up new opportunities for efficient molecular biology and genetic manipulation in Saccharomyces cerevisiae.

synthetic biology↗

bioRxiv2024

AlloPool: An Adaptive Graph Neural Network for Dynamic Allosteric Network Prediction in Protein Systems

Allosteric communication is essential to protein function, facilitating the dynamic regulation of biological responses through the propagation of structural and dynamic changes between regulatory and effector sites in response to stimuli. Traditional approaches to studying protein allostery often rely on static protein structures or abstract representations involving fully connected interaction graphs, which do not capture the temporal and state-dependent nature of these dynamic systems. Here, we introduce AlloPool, a graph neural network (GNN)-based model that iteratively prunes residue interactions to identify minimal, time-dependent interaction networks that govern long-range structural and dynamic responses to chemical or mechanical stimuli. Using temporal attention and graph aggregation, AlloPool accounts for evolving protein conformations in both molecular dynamics (MD) and steered MD (SMD) simulations to predict MD and SMD trajectories. We validate AlloPool on the Pin-1 protein and the ADGRG1 and B1AR receptors, showcasing its ability to accurately recapitulate protein motions, infer allosteric communication pathways, and identify critical allosteric sites. Additionally, AlloPool identifies force-dependent changes in GAIN domain structure and reconstructs directed information flow under mechanical load. Comparative analyses indicate that AlloPool outperforms existing models in MD and SMD trajectory reconstruction, presenting a new framework for analyzing force- and ligand-induced allosteric motions. This work advances the modeling of allosteric systems and offers broad potential for applications in drug discovery, synthetic biology, and protein engineering.

bioinformatics↗

bioRxiv2022

Computational design of ultrasensitive flexible peptide:receptor signaling complexes for enhanced chemotaxis

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor--peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve unprecedented signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

biophysics↗

bioRxiv2022

Computational rewiring of allosteric pathways reprograms GPCR selective responses to ligands

G-protein-coupled receptors (GPCRs) are the largest class of cell surface receptors and drug targets, and respond to a wide variety of chemical stimuli to activate diverse cellular functions. Understanding and predicting how ligand binding triggers a specific signaling response is critical for drug discovery and design but remains a major challenge. Here, computational design of GPCR allosteric functions is used to uncover the mechanistic relationships between agonist ligand chemistry, receptor sequence, structure, dynamics and allosteric signaling in the dopamine D2 receptor. Designed gain of function D2 variants for dopamine displayed very divergent G-protein signaling responses to other ligand agonists that strongly correlated with ligand structural similarity. Consistent with these observations, computational analysis revealed distinct topologies of allosteric signal transduction pathways for each ligand-bound D2 pair that were perturbed differently by the designs. We leveraged these findings by rewiring ligand-specific pathways and designed receptors with highly selective ligand responses. Overall, our study suggests that distinct ligand agonists can activate a given signaling effector through specific "allosteric activator" moieties that engage partially independent signal transmission networks in GPCRs. The results provide a mechanistic framework for understanding and predicting the impact of sequence polymorphism on receptor pharmacology, informing selective drug design and rationally designing receptors with highly selective ligand responses for basic and therapeutic applications.

bioengineering↗

bioRxiv2021

Computational design of highly signaling active membrane receptors through de novo solvent-mediated allosteric networks

Protein catalysis and allostery require the atomic-level orchestration and motion of residues, ligand, solvent and protein effector molecules, but the ability to design protein activity through precise protein-solvent cooperative interactions has not been demonstrated. Here, we report the design of a dozen novel membrane receptors catalyzing G-protein nucleotide exchange through diverse de novo engineered allosteric pathways mediated by cooperative networks of intra-protein, protein-ligand and solvent molecule interactions. Consistent with the predictions, designed protein activities strongly correlated with the level of solvent-mediated interaction network plasticity at flexible transmembrane helical interfaces. Several designs displayed considerably enhanced thermostability and activity compared to related natural receptors. The most stable and active variant crystallized in an unforeseen signaling active conformation, in excellent agreement with the design models. The allosteric network topologies of the best designs bear limited similarity to those of natural receptors and reveal a space of allosteric interactions larger than previously inferred from natural proteins. The approach should prove useful for engineering proteins with novel complex protein catalytic and signaling activities.

biophysics↗

bioRxiv2021

Computationally-guided tuning of ligand sensitivity in a GPCR-based sensor

Genetically-encoded fluorescent sensors for neuromodulators are increasingly used molecular tools in neuroscience. However, these protein-based biosensors are often limited by the sensitivity of the protein scaffold towards endogenous ligands. Here, we explored the possibility of applying computational design approaches for enhancing sensor sensitivity. Using the dopamine sensor dLight1 as proof of concept, we designed two variants that boost the sensors potency (EC50) for dopamine and norepinephrine by up to 5- and 15-fold, respectively. Interestingly, the largest effects were obtained through improved designed allosteric transmission in the transmembrane region of the sensor. Our approach should prove generally useful for enhancing sensing capabilities of a large variety of neuromodulator sensors.

bioengineering↗

bioRxiv2021

Computationally designed GPCR quaternary structures bias signaling pathway activation

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can now be readily characterized, receptor associations into quaternary structures are very challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting membrane receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed stabilities, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only a few specific dimer structures also recruited {beta}-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a conformational switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs {beta}-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

biophysics↗

bioRxiv2021

Isolation and characterization of barley (Hordeum vulgare) extracellular vesicles and their role in RNAi-based crop protection

The demonstration that spray-induced gene silencing (SIGS) can confer strong disease resistance bypassing the laborious and time-consuming transgenic expression of double-stranded (ds)RNA to induce gene silencing of pathogenic targets was groundbreaking. However, future field applications will require fundamental mechanistic knowledge on dsRNA uptake, processing, and its transfer. There is increasing evidence that extracellular vesicles (EVs) mediate the transfer of transgene-derived small interfering (si)RNAs in host-induced gene silencing (HIGS) applications. Here, we examined the role of EVs regarding the translocation of sprayed dsRNA from barley (Hordeum vulgare) to the target fungus Fusarium graminearum. We found barley EVs with 156 nm in size containing predominantly 21 and 19 nucleotide (nt) siRNAs starting with a 5-terminal Adenine. Notably, barley EVs contain less siRNA compared to EVs isolated from transgenic HIGS Arabidopsis plants. Together our results further underpin mechanistic differences between HIGS and SIGS applications and a minor role of EVs in SIGS.

plant biology↗

bioRxiv2020

Platon: identification and characterization of bacterial plasmid contigs in short-read draft assembliesexploiting protein-sequence-based replicon distribution scores

Plasmids are extrachromosomal genetic elements replicating independently of the chromosome which play a vital role in the environmental adaptation of bacteria. Due to potential mobilization or conjugation capabilities, plasmids are important genetic vehicles for antimicrobial resistance genes and virulence factors with huge and increasing clinical implications. They are therefore subject to large genomic studies within the scientific community worldwide. As a result of rapidly improving next generation sequencing methods, the amount of sequenced bacterial genomes is constantly increasing, in turn raising the need for specialized tools to (i) extract plasmid sequences from draft assemblies, (ii) derive their origin and distribution, and (iii) further investigate their genetic repertoire. Recently, several bioinformatic methods and tools have emerged to tackle this issue; however, a combination of both high sensitivity and specificity in plasmid sequence identification is rarely achieved in a taxon-independent manner. In addition, many software tools are not appropriate for large high-throughput analyses or cannot be included into existing software pipelines due to their technical design or software implementation. In this study, we investigated differences in the replicon distributions of protein-coding genes on a large scale as a new approach to distinguish plasmid-borne from chromosome-borne contigs. We defined and computed statistical discrimination thresholds for a new metric: the replicon distribution score (RDS) which achieved an accuracy of 96.6%. The final performance was further improved by the combination of the RDS metric with heuristics exploiting several plasmid specific higher-level contig characterizations. We implemented this workflow in a new high-throughput taxon-independent bioinformatics software tool called Platon for the recruitment and characterization of plasmid-borne contigs from short-read draft assemblies. Compared to PlasFlow, Platon achieved a higher accuracy (97.5%) and more balanced predictions (F1=82.6%) tested on a broad range of bacterial taxa and better or equal performance against the targeted tools PlasmidFinder and PlaScope on sequenced E. coli isolates. Platon is available at: platon.computational.bio Data SummaryO_LIPlaton was developed as a Python 3 command line application for Linux. C_LIO_LIThe complete source code and documentation is available on GitHub under a GPL3 license: https://github.com/oschwengers/platon and platon.computational.bio. C_LIO_LIAll database versions are hosted at Zenodo: DOI 10.5281/zenodo.3349651. C_LIO_LIPlaton is available via bioconda package platon C_LIO_LIPlaton is available via PyPI package cb-platon C_LIO_LIBacterial representative sequences for UniProts UniRef90 protein clusters, complete bacterial genome sequences from the NCBI RefSeq database, complete plasmid sequences from the NCBI genomes plasmid section, created artificial contigs, RDS threshold metrics and raw protein replicon hit counts used to create and evaluate the marker protein sequence database are hosted at Zenodo: DOI 10.5281/zenodo.3759169 C_LIO_LI24 Escherichia coli isolates sequenced with short read (Illumina MiSeq) and long read sequencing technologies (Oxford Nanopore Technology GridION platform) used for real data benchmarks are available under the following NCBI BioProjects: PRJNA505407, PRJNA387731 C_LI Impact StatementPlasmids play a vital role in the spread of antibiotic resistance and pathogenicity genes. The increasing numbers of clinical outbreaks involving resistant pathogens worldwide pushed the scientific community to increase their efforts to comprehensively investigate bacterial genomes. Due to the maturation of next-generation sequencing technologies, nowadays entire bacterial genomes including plasmids are sequenced in huge scale. To analyze draft assemblies, a mandatory first step is to separate plasmid from chromosome contigs. Recently, many bioinformatic tools have emerged to tackle this issue. Unfortunately, several tools are implemented only as interactive or web-based tools disabling them for necessary high-throughput analysis of large data sets. Other tools providing such a high-throughput implementation however often come with certain drawbacks, e.g. providing taxon-specific databases only, not providing actionable, i.e. true binary classification or achieving biased classification performances towards either sensitivity or specificity. Here, we introduce the tool Platon implementing a new replicon distribution-based approach combined with higher-level contig characterizations to address the aforementioned issues. In addition to the plasmid detection within draft assemblies, Platon provides the user with valuable information on certain higher-level contig characterizations. We show that Platon provides a balanced classification performance as well as a scalable implementation for high-throughput analyses. We therefore consider Platon to be a powerful, species-independent and flexible tool to scan large amounts of bacterial whole-genome sequencing data for their plasmid content.

bioinformatics↗