Biology subjects

Jeschek, M.

Publications and source records attributed to Jeschek, M..

5 recordsLinked to original sources

bioRxiv2024

Computer assisted multi-level optimization of malonyl-CoA availability in Pseudomonas putida

Malonyl-CoA is the major precursor for the biosynthesis of diverse industrially valuable products such as fatty acids/alcohols, flavonoids, and polyketides. However, its intracellular availability is limited in most microbial hosts, hampering the biological synthesis of such chemicals. To address this limitation, we present a multi-level optimization workflow using modern metabolic engineer-ing technologies to systematically increase the malonyl-CoA levels in Pseudomonas putida. The workflow involves the identification of gene downregulations, chassis selection, and optimization of the acetyl-CoA carboxylase complex through ribosome binding site engineering. Computa-tional tools and high-throughput screening with a malonyl-CoA biosensor enabled the rapid eval-uation of numerous genetic targets. Combining the most beneficial targets led to a 5.8-fold en-hancement in the production titer of the valuable polyketide phloroglucinol. This study demon-strates the effective integration of computational and genetic technologies for engineering P. putida, opening new avenues for the development of industrially relevant strains and the investi-gation of fundamental biological questions.

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

Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript

Translation is a key determinant of gene expression and an important biotechnological engineering target. In bacteria, 5-untranslated region (5-UTR) and coding sequence (CDS) are well-known mRNA parts controlling translation and thus cellular protein levels. However, the complex interaction of 5-UTR and CDS has so far only been studied for few sequences leading to non-generalisable and partly contradictory conclusions. Herein, we systematically assess the dynamic translation from over 1.2 million 5-UTR-CDS pairs in Escherichia coli to investigate their collective effect using a new method for ultradeep sequence-function mapping. This allows us to disentangle and precisely quantify effects of known and hypothetical sequence determinants of translation. We find that 5-UTR and CDS individually account for 53% and 20% of variance in translation, respectively, and show conclusively that, contrary to a common hypothesis, tRNA abundance does not explain expression changes between CDSs with different synonymous codons. Moreover, the obtained large-scale data clearly point to a base-pairing interaction between initiator tRNA and mRNA beyond the anticodon-codon interaction, an effect that is often masked for individual sequences and therefore inaccessible to low-throughput approaches. Our study highlights the indispensability of ultradeep sequence-function mapping to accurately determine the contribution of parts and phenomena involved in gene regulation.

synthetic biology↗

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↗

bioRxiv2020

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Predicting quantitative effects of gene regulatory elements (GREs) on gene expression is a longstanding challenge in biology. Machine learning models for gene expression prediction may be able to address this challenge, but they require experimental datasets that link large numbers of GREs to their quantitative effect. However, current methods to generate such datasets experimentally are either restricted to specific applications or limited by their technical complexity and error-proneness. Here we introduce DNA-based phenotypic recording as a widely applicable and practical approach to generate very large datasets linking GREs to quantitative functional readouts of high precision, temporal resolution, and dynamic range, solely relying on sequencing. This is enabled by a novel DNA architecture comprising a site-specific recombinase, a GRE that controls recombinase expression, and a DNA substrate modifiable by the recombinase. Both GRE sequence and substrate state can be determined in a single sequencing read, and the frequency of modified substrates amongst constructs harbouring the same GRE is a quantitative, internally normalized readout of this GREs effect on recombinase expression. Using next-generation sequencing, the quantitative expression effect of extremely large GRE sets can be assessed in parallel. As a proof of principle, we apply this approach to record translation kinetics of more than 300,000 bacterial ribosome binding sites (RBSs), collecting over 2.7 million sequence-function pairs in a single experiment. Further, we generalize from these large-scale datasets by a novel deep learning approach that combines ensembling and uncertainty modelling to predict the function of untested RBSs with high accuracy, substantially outperforming state-of-the-art methods. The combination of DNA-based phenotypic recording and deep learning represents a major advance in our ability to predict quantitative function from genetic sequence.

synthetic biology↗