Biology subjects

Hochstrasser, R.

Publications and source records attributed to Hochstrasser, R..

4 recordsLinked to original sources

bioRxiv2026

Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases

Large language models and generative protein design promise to accelerate biotechnology, but it remains unclear whether they can engineer dynamic megasynth(et)ases whose activity depends on transient, context-specific domain interfaces. Non-ribosomal peptide synthetases (NRPSs) are an especially demanding target, yet a high-value one because they produce many clinically important natural products and offer a route to analogs that are often difficult or impractical to access by chemical synthesis. Here we integrate pretrained generative models (ESM3, ProteinMPNN and EvoDiff) with design-build-test-learn cycles and data-guided prioritization to generate 76 de novo thiolation (T) domains. We built and tested 578 recombinant NRPS variants in vivo spanning minimal, full-length and hybrid assembly lines. AI-designed T-domains supported product formation across architectures, enabled catalytically active hybrids at recombined junctions and increased yields by up to [~]3-fold relative to NRPSs carrying the native T-domain. A representative design showed improved soluble expression, refolding, and a 12 {degrees}C higher melting temperature, while molecular dynamics simulations indicated preserved global stability but reshaped, state-dependent interdomain contact networks. Together, these results establish generative design as an effective route to context-conditioned optimization and reprogramming of biosynthetic assembly lines.

synthetic biology↗

bioRxiv2024

CellEKT: A robust chemical proteomics workflow to profile cellular target engagement of kinase inhibitors

The human genome encodes 518 protein kinases that are pivotal for drug discovery in various therapeutic areas such as cancer and autoimmune disorders. The majority of kinase inhibitors target the conserved ATP-binding pocket, making it difficult to develop selective inhibitors. To characterize and prioritize kinase-inhibiting drug candidates, efficient methods are desired to determine target engagement across the cellular kinome. In this study, we present CellEKT (Cellular Endogenous Kinase Targeting), an optimized and robust chemical proteomics platform for investigating cellular target engagement of endogenously expressed kinases using the sulfonyl fluoride-based probe XO44 and two new probes ALX005 and ALX011. The optimized workflow enabled the determination of the kinome interaction landscape of covalent and non-covalent drugs across over 300 kinases, expressed as half maximum inhibitory concentration (IC50), which were validated using distinct platforms like phosphoproteomics and NanoBRET. With CellEKT, target engagement profiles were linked to their substrate space. CellEKT has the ability to decrypt drug actions and to guide the discovery and development of drugs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/616061v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@5dd891org.highwire.dtl.DTLVardef@1353379org.highwire.dtl.DTLVardef@1c67382org.highwire.dtl.DTLVardef@1c964eb_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

The Legionella Lqs-LvbR regulatory network controls temperature-dependent growth onset and bacterial cell density

Legionella species are facultative intracellular pathogens, which can cause a life-threatening pneumonia termed Legionnaires disease. Legionella pneumophila employs the Legionella quorum sensing (Lqs)-LvbR network to regulate virulence and motility, but its role for growth in media is ill-defined. Compared to the parental L. pneumophila strain JR32, a {Delta}lqsR mutant showed a reduced lag phase at 30{degrees}C and reached a higher cell density at 45{degrees}C, while the {Delta}lqsA, {Delta}lqsS and {Delta}lqsT mutants exhibited a longer lag phase and reached only a lower cell density. A {Delta}lvbR mutant resumed growth like the parental strain at 30{degrees}C, but exhibited a substantially reduced cell density at 45{degrees}C. Thus, LvbR is an important cell density regulator at elevated temperatures. A quantitative analysis of temperature-dependent growth characteristics of environmental and clinical strains revealed that L. pneumophila strains grew in AYE medium after distinct lag phases with similar rates at 30{degrees}C, reached different cell densities at the optimal growth temperature of 40{degrees}C, and no longer grew at 50{degrees}C. Legionella longbeachae reached a rather low cell density at 40{degrees}C and did not grow at and beyond 45{degrees}C. Genes encoding components of the Lqs-LvbR network were present in the genomes of the environmental and clinical L. pneumophila isolates, and the PlqsR, PlqsA, PlqsS and PlvbR promoters from strain JR32 were active in these strains. Taken together, our results indicate that the Lqs-LvbR network governs the temperature-dependent growth onset and cell density of the L. pneumophila reference strain JR32, and possibly also of environmental and clinical L. pneumophila isolates. ImportanceEnvironmental bacteria of the genus Legionella are the causative agents of the severe pneumonia Legionnaires disease, the incidence of which is worldwide on the rise. Legionella pneumophila and Legionella longbeachae are the clinically most relevant species. The opportunistic pathogens are inhaled through contaminated aerosols and replicate in human lung macrophages with a similar mechanism as in their natural hosts, free-living amoebae. Given their prevalence in natural and technical water systems, an efficient control of Legionella spp. by physical, chemical or biological means will reduce the incidence of Legionnaires disease. Here we report that the Legionella quorum sensing (Lqs) system and the pleiotropic transcription factor LvbR govern the temperature-dependent growth onset and cell density of bacterial cultures. Hence, the growth of L. pneumophila in water systems is not only determined by the temperature and nutrient availability, but also by quorum sensing, i.e., density- and signaling molecule-dependent gene regulation.

microbiology↗

bioRxiv2017

Discovery and reduction of nonspecific activities of the major herbicide-resistance gene BAR

Herbicide resistance is a major trait of genetically modified (GM) crops. Currently, resistance to phosphinothricin (also known as glufosinate) is the second most widespread genetically engineered herbicide-resistance trait in crops after glyphosate resistance1,2. Resistance to phosphinothricin in plants is achieved by transgenic expression of the bialaphos resistance (BAR) or phosphinothricin acetyltransferase (PAT) genes, which were initially isolated from the natural herbicide bialaphos-producing soil bacteria Streptomyces hygroscopicus and S. viridochromogenes, respectively3,4. Mechanistically, BAR and PAT encode phosphinothricin acetyltransferase, which transfers an acetyl group from acetyl coenzyme A (acetyl-CoA) to the -NH2 group of phosphinothricin, resulting in herbicide inactivation1. Although early in vitro enzyme assays showed that recombinant BAR and PAT exhibit substrate preference toward phosphinothricin over the 20 proteinogenic amino acids1, whether transgenic expression of BAR and PAT affects plant endogenous metabolism in vivo was not known. Combining metabolomics, plant genetics, and biochemical approaches, we show that transgenic BAR indeed converts two plant endogenous amino acids, aminoadipate and tryptophan, to their respective N-acetylated products in several plant species examined. We report the crystal structures of BAR, and further delineate structural basis for its substrate selectivity and catalytic mechanism. Through structure-guided protein engineering, we generated several BAR variants that display significantly reduced nonspecific activities compared to its wild-type counterpart. Our results demonstrate that transgenic expression of enzymes as a common strategy in modern biotechnology may render unintended metabolic consequences arisen from enzyme promiscuity. Understanding of such phenomena at the mechanistic level will facilitate better design of maximally insulated systems featuring heterologously expressed enzymes.

plant biology↗