Biology subjects

Hansen, L. G.

Publications and source records attributed to Hansen, L. G..

3 recordsLinked to original sources

bioRxiv2024

Metabolic engineering of yeast for de novo production of kratom monoterpene indole alkaloids

Monoterpene indole alkaloids (MIAs) from Mitragyna speciosa ("kratom"), such as mitragynine and speciogynine, are promising novel scaffolds for opioid receptor ligands for treatment of pain, addiction, and depression. While kratom leaves have been used for centuries in South-East Asia as stimulant and pain management substance, the biosynthetic pathway of these psychoactives have only recently been partially elucidated. Here, we demonstrate the de novo production of mitragynine and speciogynine in Saccharomyces cerevisiae through the reconstruction of a five-step synthetic pathway from common MIA precursor strictosidine comprising fungal tryptamine 4-monooxygenase to bypass an unknown kratom hydroxylase. Upon optimizing cultivation conditions, a titer of [~]290 {micro}g/L kratom MIAs from glucose was achieved. Untargeted metabolomics analysis of lead production strains led to the identification of numerous shunt products derived from the activity of strictosidine synthase (STR) and dihydrocorynantheine synthase (DCS), highlighting them as candidates for enzyme engineering to further improve kratom MIAs production in yeast. Finally, by feeding fluorinated tryptamine and expressing a human tailoring enzyme, we further demonstrate production of fluorinated and hydroxylated mitragynine derivatives with potential applications in drug discovery campaigns. Altogether, this study introduces a yeast cell factory platform for the biomanufacturing of complex natural and new-to-nature kratom MIAs derivatives with therapeutic potential.

synthetic biology↗

bioRxiv2024

Harnessing Chemical Space Neural Networks to Systematically Annotate GPCR ligands

Machine learning (ML) has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors (hGPCRs) in FDA-approved drugs, exhaustive in-distribution drug-target interaction (DTI) testing across all pairs of hGPCRs and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution (OOD) exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks (CSNN) that leverages network homophily and training-free graph neural networks (GNNs) with Labels as Features (LaF). We show that CSNNs ability to make accurate predictions strongly correlates with network homophily. Thus, LaFs strongly increase a ML models capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 DTIs, 539 compounds, 7 hGPCRs) to discover novel DTIs for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

bioinformatics↗

bioRxiv2023

Literate programming for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of complex biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of a metabolic pathway designs for production of a key precursor to medicinal alkaloids. The teemi platform is publicly available at PyPi and GitHub.

bioinformatics↗