Biology subjects

Panke, S.

Publications and source records attributed to Panke, S..

7 recordsLinked to original sources

bioRxiv2026

Chimerophore antibiotics: engineered multimodal host defense peptides

Ribosomally synthesized host defense peptides (HDPs) are promising candidates for novel antibiotics. However, non-lytic HDPs, which target intracellular machinery, remain underexploited due to limitations including low potency in serum and narrow activity spectra. To enhance their therapeutic profile, we fused non-lytic HDPs generating "chimerophores" with multimodal mechanisms of action (MOAs). Using a high-throughput self-screening platform (Mex), we synthesized and evaluated a combinatorial library of 99,235 variants, identifying over 30,300 active chimerophores, vastly expanding the functional space of chimeric HDPs. Functional screening of 18 chimerophores revealed candidates with potent, broad-spectrum activity displaying serum-tolerance, low cytotoxicity, orthogonal uptake pathways and multimodality, such as simultaneously targeting of ribosomes and DNA. Integrating these distinct mechanisms into a single molecule allowed lead candidate cp9 to suppress the emergence of resistance in Pseudomonas aeruginosa, establishing a scalable platform for the systematic engineering of next-generation multimodal antibiotics.

microbiology↗

bioRxiv2025

Peptide Barcodes for miRNA activity assessment in mammalian cells

Studies of gene regulation require measurements of mRNA and protein levels of a regulated gene. Recently, parallel reporter assays have been introduced to study the regulation of multiple genes at once. While transcriptional regulation can be probed by next generation sequencing, post-transcriptional regulation requires the ability to measure multiple proteins in the same experiment. Multiplexing with the help of fluorescent proteins limits the addressable diversity of simultaneous measurements due to spectral overlap. Inspired by the utility of proteotypic peptides in targeted proteomics, here we show that genetically encoded peptide reporters (peptide barcodes) can be used to analyze multiple post-transcriptional pathways in parallel. We use RNA interference as an exemplary regulatory mechanism that occurs on both transcriptional and post-transcriptional levels. We measure the activity of multiple microRNAs in parallel using a peptide barcode-based miRNA sensor library. Fluorescent reporters are used to validate the accuracy of the miRNA activities reported via the peptide barcodes. Several assay optimization steps are explored leading to the robust activity profiling of nine miRNAs across three different cell lines. Overall, this study underlines the multiplexing potential of peptide barcodes to rapidly and quantitatively measure the post-transcriptional regulation.

synthetic biology↗

bioRxiv2024

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Tailored enzymes hold great potential to accelerate the transition to a sustainable bioeconomy. Yet, enzyme engineering remains challenging as it relies largely on serendipity and is, therefore, highly laborious and prone to failure. The efficiency and success rates of engineering campaigns may be improved substantially by applying machine learning to construct a comprehensive representation of the sequence-activity landscape from small sets of experimental data. However, it often proves challenging to reliably model a large protein sequence space while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning and model-guided library design. We applied this strategy to efficiently engineer an artificial metalloenzyme (ArM) catalysing a new-to-nature hydroamination reaction. By combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds according to user-defined objectives. Crucial characteristics of our enhanced enzyme engineering pipeline include i) the cost-effective generation of information-rich experimental data sets, ii) the integration of an explorative round to improve the performance of the model, as well as iii) the consideration of experimental noise during modelling. Our approach led to an order-of-magnitude boost in the hit rate of screening while making efficient use of experimental resources. Smart search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.

synthetic biology↗

bioRxiv2022

Systematic investigation of synthetic operon designs enables prediction and control of expression levels of multiple proteins

Controlling the expression levels of multiple recombinant proteins for optimal performance is crucial for synthetic biosystems but remains difficult given the large number of DNA-encoded factors that influence the process of gene expression from transcription to translation. In bacterial hosts, biosystems can be economically encoded as operons, but the sequence requirements for exact tuning of expression levels in an operon remain unclear. Here, we demonstrate the extent and predictability of protein-level variation using diverse arrangements of twelve genes to generate 88 synthetic operons with up to seven genes at varying inducer concentrations. The resulting 2772 protein expression measurements allowed the training of a sequence-based machine learning model that explains 83% of the variation in the data with a mean absolute error of 9% relative to reference constructs, making it a useful tool for protein expression prediction. Feature importance analysis indicates that operon length, gene position and gene junction structure are of major importance for protein expression.

synthetic biology↗

bioRxiv2022

Signal peptide efficiency: from high-throughput data to prediction and explanation

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ~12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the in silico evaluation of signal peptides.

synthetic biology↗

bioRxiv2021

A multiplexed cell-free assay to screen for antimicrobial peptides in double emulsion droplets

The global surge in bacterial resistance against traditional antibiotics triggered intensive research for novel compounds, with antimicrobial peptides (AMPs) identified as a promising candidate. Automated methods to systematically generate and screen AMPs according to their membrane preference, however, are still lacking. We introduce a novel microfluidic system for the simultaneous cell-free production and screening of AMPs for their membrane specificity. On our device, AMPs are cell-free produced within water-in-oil-in-water double emulsion droplets, generated at high frequency. Within each droplet, the peptides can interact with different classes of co-encapsulated liposomes, generating a membrane-specific fluorescent signal. The double emulsions can be incubated and observed in a hydrodynamic trapping array or analysed via flow cytometry. Our approach provides a valuable tool for the discovery and development of membrane-active antimicrobials.

bioengineering↗

bioRxiv2020

Systematic Engineering of Artificial Metalloenzymes for New-to-Nature Reactions

Artificial metalloenzymes (ArMs) catalyzing new-to-nature reactions under mild conditions could play an important role in the transition to a sustainable, circular economy. While ArMs have been created for a variety of bioorthogonal transformations, attempts at optimizing their performance by enzyme engineering have been case-specific and resulted only in modest improvements. To realize the full potential of ArMs, methods that enable the rapid discovery of highly active ArM variants for any reaction of interest are required. Here, we introduce a broadly applicable, automation-compatible ArM engineering platform, which relies on periplasmic compartmentalization in Escherichia coli to rapidly and reliably identify improved ArM variants based on the biotin-streptavidin technology. We systematically assess 400 ArM mutants for five bioorthogonal transformations involving different metal cofactors, reaction mechanisms and substrate-product pairs, including novel ArMs for gold-catalyzed hydroamination and hydroarylation. The achieved activity enhancements of up to fifteen-fold over wild type highlight the potential of the systematic approach to ArM engineering. We further capitalize on the sequence-activity data to suggest and validate smart strategies for future screening campaigns. This systematic, multi-reaction study has important implications for the development of highly active ArMs for novel applications in biocatalysis and synthetic biology.

synthetic biology↗